From e3061f3bff1b9e47c459540c22bb11920cb1fe32 Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Thu, 17 Apr 2025 20:36:00 -0700 Subject: [PATCH 01/13] New branch Ladygin --- Ladygin/README.md | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 Ladygin/README.md diff --git a/Ladygin/README.md b/Ladygin/README.md new file mode 100644 index 00000000..cbd01009 --- /dev/null +++ b/Ladygin/README.md @@ -0,0 +1,37 @@ +# K-point convergence tracker (Materials) + +> Ideal candidate: scientists skilled in Density Functional Theory and proficient in python. + +# Overview + +The aim of this task is to create a python package that implements automatic convergence tracking mechanism for a materials simulations engine. The convergence is tracked with respect to the k-point sampling inside a reciprocal cell of a crystalline compound. + +# Requirements + +1. automatically find the dimensions of a k-point mesh that satisfy a certain criteria for total energy (eg. total energy is converged within dE = 0.01meV) +1. the code shall be written in a way that can facilitate easy addition of convergence wrt other characteristics extracted from simulations (forces, pressures, phonon frequencies etc) +1. the code shall support VASP or Quantum ESPRESSO + +# Expectations + +- correctly find k-point mesh that satisfies total energy convergence parameters for a set of 10 materials, starting from Si2, as simplest, to a 10-20-atom supercell of your choice +- modular and object-oriented implementation +- commit early and often - at least once per 24 hours + +# Timeline + +We leave exact timing to the candidate. Must fit Within 5 days total. + +# User story + +As a user of this software I can start it passing: + +- path to input data (eg. pw.in / POSCAR, INCAR, KPOINTS) and +- kinetic energy cutoff + +as parameters and get the k-point dimensions (eg. 5 5 5). + +# Notes + +- create an account at exabyte.io and use it for the calculation purposes +- suggested modeling engine: Quantum ESPRESSO From c68b80a9774ad3df8fa12536c3f1b2903298a405 Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Thu, 17 Apr 2025 23:15:48 -0700 Subject: [PATCH 02/13] Finished Day 1 with base classes and tests --- Ladygin/Changes.txt | 4 + Ladygin/ConvergenceTracker/__init__.py | 0 .../convergence_tracker_base.py | 52 + .../convergence_tracker_qe.py | 59 + Ladygin/ConvergenceTracker/driver.py | 87 + Ladygin/ConvergenceTracker/pw.in | 0 Ladygin/ConvergenceTracker/utils.py | 1 + Ladygin/test_case/Si-PBE.upf | 6765 +++++++++++++++++ Ladygin/test_case/pw.in | 31 + Ladygin/test_case/pw.out | 280 + Ladygin/test_case/run.sh | 23 + 11 files changed, 7302 insertions(+) create mode 100644 Ladygin/Changes.txt create mode 100644 Ladygin/ConvergenceTracker/__init__.py create mode 100644 Ladygin/ConvergenceTracker/convergence_tracker_base.py create mode 100644 Ladygin/ConvergenceTracker/convergence_tracker_qe.py create mode 100644 Ladygin/ConvergenceTracker/driver.py create mode 100644 Ladygin/ConvergenceTracker/pw.in create mode 100644 Ladygin/ConvergenceTracker/utils.py create mode 100644 Ladygin/test_case/Si-PBE.upf create mode 100644 Ladygin/test_case/pw.in create mode 100644 Ladygin/test_case/pw.out create mode 100755 Ladygin/test_case/run.sh diff --git a/Ladygin/Changes.txt b/Ladygin/Changes.txt new file mode 100644 index 00000000..4eca4b69 --- /dev/null +++ b/Ladygin/Changes.txt @@ -0,0 +1,4 @@ +Day 1; 04/17/25 + +- Made base and qe classes for driver and tracker +- Tested on base case of Si diff --git a/Ladygin/ConvergenceTracker/__init__.py b/Ladygin/ConvergenceTracker/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_base.py b/Ladygin/ConvergenceTracker/convergence_tracker_base.py new file mode 100644 index 00000000..bfea33cf --- /dev/null +++ b/Ladygin/ConvergenceTracker/convergence_tracker_base.py @@ -0,0 +1,52 @@ +import pickle +import os +from driver import Driver + + +class ConvergenceTracker(): + """Base class for convergence tracker + Contains methods nessasure for any conv tracker""" + + def __init__(self, workdir: str = './', target:str = 'total_energy', encut: float = 600., eps = 1e-2) -> None: + """workdir - directory for input files and calculations + target - target property to optimize + encut - kinetic energy cutoff (in eV) + eps - convergence criteria (in meV) + """ + + self.workdir = workdir + self.target = target + self.encut = encut + self.eps = eps + self.kpoint_opt = None # optimal kpoint value + + def find_opt(self, driver: Driver) -> None: + """Finds optimal kpoint by running simulations and refining the parameter until convergence reached""" + pass + + def save(self, name:str) -> None: + """save class as self.name.dat""" + self.name = name + file = open(self.name+'.dat','wb') + pickle.dump(self.__dict__, file) + file.close() + + def load(self) -> None: + """try load self.name.dat""" + + file = open(self.name+'.dat','rb') + + self.__dict__ = pickle.load(file) + + def __repr__(self) -> str: + return f"Convergence Tracker of {self.target}) with eps equal to {self.eps} with results saved at {self.workdir}" + + +# Usage examples +if __name__ == "__main__": + Tracker = ConvergenceTracker() + print(Tracker) + Tracker.save('TestTracker') + Tracker.load() + print(Tracker) + os.remove('TestTracker.dat') \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py new file mode 100644 index 00000000..100e0d0d --- /dev/null +++ b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py @@ -0,0 +1,59 @@ +from convergence_tracker_base import ConvergenceTracker +from driver import DriverQE +from utils import consts + +class ConvergenceTrackerQE(ConvergenceTracker): + """Implimentation of convergence tracker for Quantum Espresso Simulation""" + + def __init__(self, workdir: str = './', target:str = 'total_energy', encut: float = 600., eps = 1e-2) -> None: + """workdir - directory for input files and calculations + target - target property to optimize + encut - kinetic energy cutoff (in eV) + eps - convergence criteria (in meV) + """ + super().__init__(workdir, target, encut, eps) + + def find_opt(self, driver: DriverQE = DriverQE()) -> None: + """Finds optimal kpoint by running simulations and refining the parameter until convergence reached + DriverQE - calculation driver for quantum espresso + + """ + + # Start with 2 kpoints to estimate error + kpoint_list = [2, 4] + target_list = [] + + driver.gen_input(self.encut, kpoint_list[0]) + driver.run() + target_list.append(driver.extract_target(self.target)) + + driver.gen_input(self.encut, kpoint_list[1]) + driver.run() + target_list.append(driver.extract_target(self.target)) + + + error = [abs(target_list[-1] - target_list[-2])] + + # while converge go increments of 2 + num_iter = 10 + i = 0 + while error[-1] > self.eps * 1e-3 and i < num_iter: + i+= 1 + + kpoint_list.append(kpoint_list[-1] + 2) + driver.gen_input(self.encut/consts['Ry'], kpoint_list[-1]) + driver.run() + target_list.append(driver.extract_target(self.target)) + + error.append(abs(target_list[-1] - target_list[-2])) + + if error[-1] < self.eps: + self.kpoint_opt = kpoint_list[-1] + + +if __name__ == """__main__""": + Tracker = ConvergenceTrackerQE('./', 'total_energy', 40*consts['Ry']) + + Tracker.find_opt() + + print(f"Optimal value of kpoint is {Tracker.kpoint_opt}") \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/driver.py b/Ladygin/ConvergenceTracker/driver.py new file mode 100644 index 00000000..0711ff52 --- /dev/null +++ b/Ladygin/ConvergenceTracker/driver.py @@ -0,0 +1,87 @@ +from ase.io import read +import subprocess as sp + +class Driver(): + """Base class for running simulation""" + + def __init__(self, input_file: str) -> None: + """input_file - simulation settings""" + self.input_file = input_file + pass + + + def gen_input(self, encut: float, kpoint: float): + """Generates the input file for the driver based on input parameters + + encut - energy cutoff (in Ry) + kpoint - kpoint dimension + """ + pass + + def run(self) -> None: + """Runs point simulation in the current folder""" + pass + + + def extract_target(self, target:str) -> float: + """Extracts the target from the simulation output""" + + +class DriverQE(Driver): + + def __init__(self, input_file: str = 'pw.in') -> None: + super().__init__(input_file) + + def gen_input(self, encut: float, kpoint: float): + + with open(self.input_file, 'w') as f: + f.write(f""" &control + calculation = 'scf' + prefix = 'si' + restart_mode = 'from_scratch' + wf_collect = .true. + pseudo_dir = './' + outdir = './' + tprnfor = .true. + tstress = .true. + / + &system + ibrav = 2 + celldm(1) = 10.262 + nat = 2 + ntyp = 1 + ecutwfc = {encut} + nbnd = 8 + / + &electrons + diagonalization = 'david' + mixing_beta = 0.7 + conv_thr = 1.0d-13 + / + +ATOMIC_SPECIES + Si 28.0855 Si-PBE.upf +ATOMIC_POSITIONS crystal +Si 0.00 0.00 0.00 +Si -0.25 0.75 -0.25 +K_POINTS automatic +{kpoint} {kpoint} {kpoint} 0 0 0""") + + def run(self) -> None: + + sp.run("srun -n 4 -c 2 /pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin/pw.x -nk 4 -input pw.in > pw.out", shell = True) + + def extract_target(self, target:str) -> float: + data = read('pw.out') + if target == 'total_energy': + return data.get_total_energy() + + + +if __name__ == '__main__': + driver = DriverQE() + + driver.gen_input(40, 4) + driver.run() + total_energy = driver.extract_target('total_energy') + print("Total energy is equal to", total_energy, 'eV') diff --git a/Ladygin/ConvergenceTracker/pw.in b/Ladygin/ConvergenceTracker/pw.in new file mode 100644 index 00000000..e69de29b diff --git a/Ladygin/ConvergenceTracker/utils.py b/Ladygin/ConvergenceTracker/utils.py new file mode 100644 index 00000000..af1e4211 --- /dev/null +++ b/Ladygin/ConvergenceTracker/utils.py @@ -0,0 +1 @@ +consts = {'Ry': 13.6} \ No newline at end of file diff --git a/Ladygin/test_case/Si-PBE.upf b/Ladygin/test_case/Si-PBE.upf new file mode 100644 index 00000000..0bba6c59 --- /dev/null +++ b/Ladygin/test_case/Si-PBE.upf @@ -0,0 +1,6765 @@ + + + +This pseudopotential file has been produced using the code +ONCVPSP (Optimized Norm-Conservinng Vanderbilt PSeudopotential) +fully-relativistic version 3.3.0 08/16/2017 by D. R. Hamann +The code is available through a link at URL www.mat-simresearch.com. +Documentation with the package provides a full discription of the +input data below. + + +While it is not required under the terms of the GNU GPL, it is +suggested that you cite D. R. Hamann, Phys. Rev. B 88, 085117 (2013) +in any publication using these pseudopotentials. + + +# ATOM AND REFERENCE CONFIGURATION +# atsym z nc nv iexc psfile +Si 14.00 3 2 4 both +# +# n l f energy (Ha) +1 0 2.00 +2 0 2.00 +2 1 6.00 +3 0 2.00 +3 1 2.00 +# +# PSEUDOPOTENTIAL AND OPTIMIZATION +# lmax +2 +# +# l, rc, ep, ncon, nbas, qcut +0 1.60000 -0.39735 4 9 6.40000 +1 1.71000 -0.14959 4 9 6.40000 +2 1.90000 0.05000 4 9 6.40000 +# +# LOCAL POTENTIAL +# lloc, lpopt, rc(5), dvloc0 +4 5 1.60000 0.00000 +# +# VANDERBILT-KLEINMAN-BYLANDER PROJECTORs +# l, nproj, debl +0 2 2.50000 +1 2 2.49961 +2 2 1.50000 +# +# MODEL CORE CHARGE +# icmod, fcfact, rcfact +3 4.00000 1.30000 +# +# LOG DERIVATIVE ANALYSIS +# epsh1, epsh2, depsh +-12.00 12.00 0.02 +# +# OUTPUT GRID +# rlmax, drl +6.00 0.01 +# +# TEST CONFIGURATIONS +# ncnf +0 +# nvcnf +# n l f + + + + + + + + +0.0000 0.0100 0.0200 0.0300 0.0400 0.0500 0.0600 0.0700 +0.0800 0.0900 0.1000 0.1100 0.1200 0.1300 0.1400 0.1500 +0.1600 0.1700 0.1800 0.1900 0.2000 0.2100 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index 00000000..1481d1f8 --- /dev/null +++ b/Ladygin/test_case/pw.in @@ -0,0 +1,31 @@ + &control + calculation = 'scf' + prefix = 'si' + restart_mode = 'from_scratch' + wf_collect = .true. + pseudo_dir = './' + outdir = './' + tprnfor = .true. + tstress = .true. + / + &system + ibrav = 2 + celldm(1) = 10.262 + nat = 2 + ntyp = 1 + ecutwfc = 40.0 + nbnd = 8 + / + &electrons + diagonalization = 'david' + mixing_beta = 0.7 + conv_thr = 1.0d-13 + / + +ATOMIC_SPECIES + Si 28.0855 Si-PBE.upf +ATOMIC_POSITIONS crystal +Si 0.00 0.00 0.00 +Si -0.25 0.75 -0.25 +K_POINTS automatic +18 18 18 0 0 0 \ No newline at end of file diff --git a/Ladygin/test_case/pw.out b/Ladygin/test_case/pw.out new file mode 100644 index 00000000..fa342316 --- /dev/null +++ b/Ladygin/test_case/pw.out @@ -0,0 +1,280 @@ + + Program PWSCF v.7.3.1 starts on 17Apr2025 at 23: 8:23 + + This program is part of the open-source Quantum ESPRESSO suite + for quantum simulation of materials; please cite + "P. Giannozzi et al., J. Phys.:Condens. Matter 21 395502 (2009); + "P. Giannozzi et al., J. Phys.:Condens. Matter 29 465901 (2017); + "P. Giannozzi et al., J. Chem. Phys. 152 154105 (2020); + URL http://www.quantum-espresso.org", + in publications or presentations arising from this work. More details at + http://www.quantum-espresso.org/quote + + Parallel version (MPI & OpenMP), running on 4 processor cores + Number of MPI processes: 4 + Threads/MPI process: 1 + + MPI processes distributed on 1 nodes + 490329 MiB available memory on the printing compute node when the environment starts + + Reading input from pw.in + + Current dimensions of program PWSCF are: + Max number of different atomic species (ntypx) = 10 + Max number of k-points (npk) = 40000 + Max angular momentum in pseudopotentials (lmaxx) = 4 + + K-points division: npool = 4 + Subspace diagonalization in iterative solution of the eigenvalue problem: + a serial algorithm will be used + + + G-vector sticks info + -------------------- + sticks: dense smooth PW G-vecs: dense smooth PW + Sum 583 583 187 9257 9257 1687 + + Using Slab Decomposition + + + + bravais-lattice index = 2 + lattice parameter (alat) = 10.2620 a.u. + unit-cell volume = 270.1693 (a.u.)^3 + number of atoms/cell = 2 + number of atomic types = 1 + number of electrons = 8.00 + number of Kohn-Sham states= 8 + kinetic-energy cutoff = 40.0000 Ry + charge density cutoff = 160.0000 Ry + scf convergence threshold = 1.0E-13 + mixing beta = 0.7000 + number of iterations used = 8 plain mixing + Exchange-correlation= PBE + ( 1 4 3 4 0 0 0) + + celldm(1)= 10.262000 celldm(2)= 0.000000 celldm(3)= 0.000000 + celldm(4)= 0.000000 celldm(5)= 0.000000 celldm(6)= 0.000000 + + crystal axes: (cart. coord. in units of alat) + a(1) = ( -0.500000 0.000000 0.500000 ) + a(2) = ( 0.000000 0.500000 0.500000 ) + a(3) = ( -0.500000 0.500000 0.000000 ) + + reciprocal axes: (cart. coord. in units 2 pi/alat) + b(1) = ( -1.000000 -1.000000 1.000000 ) + b(2) = ( 1.000000 1.000000 1.000000 ) + b(3) = ( -1.000000 1.000000 -1.000000 ) + + + PseudoPot. # 1 for Si read from file: + ./Si-PBE.upf + MD5 check sum: d8e8ea3dd55db7858206a05f373c9c65 + Pseudo is Norm-conserving + core correction, Zval = 4.0 + Generated using ONCVPSP code by D. R. Hamann + Using radial grid of 1528 points, 6 beta functions with: + l(1) = 0 + l(2) = 0 + l(3) = 1 + l(4) = 1 + l(5) = 2 + l(6) = 2 + + atomic species valence mass pseudopotential + Si 4.00 28.08550 Si( 1.00) + + 48 Sym. Ops., with inversion, found (24 have fractional translation) + + + + Cartesian axes + + site n. atom positions (alat units) + 1 Si tau( 1) = ( 0.0000000 0.0000000 0.0000000 ) + 2 Si tau( 2) = ( 0.2500000 0.2500000 0.2500000 ) + + number of k points= 195 + + Number of k-points >= 100: set verbosity='high' to print them. + + Dense grid: 9257 G-vectors FFT dimensions: ( 32, 32, 32) + + Estimated max dynamical RAM per process > 15.37 MB + + Estimated total dynamical RAM > 54.71 MB + + Initial potential from superposition of free atoms + + starting charge 7.9988, renormalised to 8.0000 + Starting wfcs are 8 randomized atomic wfcs + + total cpu time spent up to now is 0.4 secs + + Self-consistent Calculation + + iteration # 1 ecut= 40.00 Ry beta= 0.70 + Davidson diagonalization with overlap + ethr = 1.00E-02, avg # of iterations = 4.8 + + Threshold (ethr) on eigenvalues was too large: + Diagonalizing with lowered threshold + + Davidson diagonalization with overlap + ethr = 6.54E-04, avg # of iterations = 1.8 + + total cpu time spent up to now is 1.8 secs + + total energy = -16.92187663 Ry + estimated scf accuracy < 0.05593641 Ry + + iteration # 2 ecut= 40.00 Ry beta= 0.70 + Davidson diagonalization with overlap + ethr = 6.99E-04, avg # of iterations = 1.0 + + total cpu time spent up to now is 2.3 secs + + total energy = -16.92416530 Ry + estimated scf accuracy < 0.00216751 Ry + + iteration # 3 ecut= 40.00 Ry beta= 0.70 + Davidson diagonalization with overlap + ethr = 2.71E-05, avg # of iterations = 4.5 + + total cpu time spent up to now is 3.0 secs + + total energy = -16.92427667 Ry + estimated scf accuracy < 0.00003903 Ry + + iteration # 4 ecut= 40.00 Ry beta= 0.70 + Davidson diagonalization with overlap + ethr = 4.88E-07, avg # of iterations = 3.8 + + total cpu time spent up to now is 3.8 secs + + total energy = -16.92428537 Ry + estimated scf accuracy < 0.00000018 Ry + + iteration # 5 ecut= 40.00 Ry beta= 0.70 + Davidson diagonalization with overlap + ethr = 2.31E-09, avg # of iterations = 3.6 + + total cpu time spent up to now is 4.5 secs + + total energy = -16.92428567 Ry + estimated scf accuracy < 0.00000001 Ry + + iteration # 6 ecut= 40.00 Ry beta= 0.70 + Davidson diagonalization with overlap + ethr = 1.32E-10, avg # of iterations = 2.1 + + total cpu time spent up to now is 5.1 secs + + total energy = -16.92428568 Ry + estimated scf accuracy < 2.4E-10 Ry + + iteration # 7 ecut= 40.00 Ry beta= 0.70 + Davidson diagonalization with overlap + ethr = 3.01E-12, avg # of iterations = 2.9 + + total cpu time spent up to now is 5.8 secs + + total energy = -16.92428568 Ry + estimated scf accuracy < 2.3E-11 Ry + + iteration # 8 ecut= 40.00 Ry beta= 0.70 + Davidson diagonalization with overlap + ethr = 2.81E-13, avg # of iterations = 2.3 + + total cpu time spent up to now is 6.4 secs + + total energy = -16.92428568 Ry + estimated scf accuracy < 1.6E-13 Ry + + iteration # 9 ecut= 40.00 Ry beta= 0.70 + Davidson diagonalization with overlap + ethr = 1.00E-13, avg # of iterations = 1.1 + + total cpu time spent up to now is 6.9 secs + + End of self-consistent calculation + + Number of k-points >= 100: set verbosity='high' to print the bands. + + highest occupied, lowest unoccupied level (ev): 6.2319 6.8136 + +! total energy = -16.92428568 Ry + estimated scf accuracy < 8.4E-15 Ry + + The total energy is the sum of the following terms: + one-electron contribution = 4.96250654 Ry + hartree contribution = 1.09491799 Ry + xc contribution = -6.18405500 Ry + ewald contribution = -16.79765521 Ry + + convergence has been achieved in 9 iterations + + Forces acting on atoms (cartesian axes, Ry/au): + + atom 1 type 1 force = 0.00000000 0.00000000 0.00000000 + atom 2 type 1 force = 0.00000000 0.00000000 0.00000000 + + Total force = 0.000000 Total SCF correction = 0.000000 + + + Computing stress (Cartesian axis) and pressure + + total stress (Ry/bohr**3) (kbar) P= 19.79 + 0.00013455 -0.00000000 -0.00000000 19.79 -0.00 -0.00 + -0.00000000 0.00013455 0.00000000 -0.00 19.79 0.00 + -0.00000000 0.00000000 0.00013455 -0.00 0.00 19.79 + + + Writing all to output data dir ./si.save/ : + XML data file, charge density, pseudopotentials, collected wavefunctions + + init_run : 0.25s CPU 0.27s WALL ( 1 calls) + electrons : 5.88s CPU 6.50s WALL ( 1 calls) + forces : 0.12s CPU 0.12s WALL ( 1 calls) + stress : 0.42s CPU 0.42s WALL ( 1 calls) + + Called by init_run: + wfcinit : 0.18s CPU 0.19s WALL ( 1 calls) + potinit : 0.02s CPU 0.04s WALL ( 1 calls) + hinit0 : 0.04s CPU 0.04s WALL ( 1 calls) + + Called by electrons: + c_bands : 4.98s CPU 5.56s WALL ( 10 calls) + sum_band : 0.80s CPU 0.82s WALL ( 10 calls) + v_of_rho : 0.09s CPU 0.10s WALL ( 10 calls) + mix_rho : 0.01s CPU 0.01s WALL ( 10 calls) + + Called by c_bands: + init_us_2 : 0.20s CPU 0.21s WALL ( 1127 calls) + cegterg : 4.24s CPU 4.36s WALL ( 490 calls) + + Called by *egterg: + cdiaghg : 0.09s CPU 0.09s WALL ( 1758 calls) + h_psi : 4.03s CPU 4.14s WALL ( 1856 calls) + g_psi : 0.01s CPU 0.01s WALL ( 1317 calls) + + Called by h_psi: + h_psi:calbec : 0.14s CPU 0.14s WALL ( 1856 calls) + vloc_psi : 3.72s CPU 3.83s WALL ( 1856 calls) + add_vuspsi : 0.15s CPU 0.15s WALL ( 1856 calls) + + General routines + calbec : 0.16s CPU 0.16s WALL ( 2101 calls) + fft : 0.03s CPU 0.04s WALL ( 125 calls) + ffts : 0.00s CPU 0.00s WALL ( 10 calls) + fftw : 3.96s CPU 4.06s WALL ( 25208 calls) + + Parallel routines + + PWSCF : 6.79s CPU 7.53s WALL + + + This run was terminated on: 23: 8:30 17Apr2025 + +=------------------------------------------------------------------------------= + JOB DONE. +=------------------------------------------------------------------------------= diff --git a/Ladygin/test_case/run.sh b/Ladygin/test_case/run.sh new file mode 100755 index 00000000..35166c3e --- /dev/null +++ b/Ladygin/test_case/run.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +module load python +conda activate tdep +source /pscratch/sd/v/vladygin/doped-Si_project/MLFF_TDEP/testbench/bin/activate + +export SLURM_CPU_BIND="cores" +export OMP_PROC_BIND=spread +export OMP_PLACES=threads +export OMP_NUM_THREADS=1 +export HDF5_USE_FILE_LOCKING=FALSE + +module load PrgEnv-nvidia +module load cuda +module unload cudatoolkit/12.2 +#module load nvhpc/23.1 +module load cray-libsci/23.02.1.1 +module load cray-hdf5-parallel +module load python/3.11 + +module swap gpu cpu + +python ../convergence_tracker_qe.py From 59e9cdce9e7c54bbbe01ad89035e776f896be426 Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Thu, 17 Apr 2025 23:19:53 -0700 Subject: [PATCH 03/13] Deleted testing file --- Ladygin/ConvergenceTracker/pw.in | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 Ladygin/ConvergenceTracker/pw.in diff --git a/Ladygin/ConvergenceTracker/pw.in b/Ladygin/ConvergenceTracker/pw.in deleted file mode 100644 index e69de29b..00000000 From e66fb06bb146b7aa82f7c867251a7bcf2c0d1cd8 Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Fri, 18 Apr 2025 23:05:45 -0700 Subject: [PATCH 04/13] - Polished the code - Finished the parser - Extracted the Scheduler to separate file - Organized as a lib - Created main script --- Ladygin/Changes.txt | 11 ++ Ladygin/ConvTrack | 45 +++++++ Ladygin/ConvergenceTracker/__init__.py | 6 + .../convergence_tracker_base.py | 16 ++- .../convergence_tracker_qe.py | 78 ++++++----- Ladygin/ConvergenceTracker/driver.py | 102 ++++++++------ Ladygin/ConvergenceTracker/exceptions.py | 11 ++ .../ConvergenceTracker/kpoint_scheduler.py | 44 +++++++ Ladygin/ConvergenceTracker/utils.py | 124 +++++++++++++++++- Ladygin/test_case/{ => pseudo}/Si-PBE.upf | 0 Ladygin/test_case/pw.in | 67 ++++++---- Ladygin/test_case/run.sh | 4 +- 12 files changed, 398 insertions(+), 110 deletions(-) create mode 100644 Ladygin/ConvTrack create mode 100644 Ladygin/ConvergenceTracker/exceptions.py create mode 100644 Ladygin/ConvergenceTracker/kpoint_scheduler.py rename Ladygin/test_case/{ => pseudo}/Si-PBE.upf (100%) diff --git a/Ladygin/Changes.txt b/Ladygin/Changes.txt index 4eca4b69..ca2f1d2d 100644 --- a/Ladygin/Changes.txt +++ b/Ladygin/Changes.txt @@ -2,3 +2,14 @@ Day 1; 04/17/25 - Made base and qe classes for driver and tracker - Tested on base case of Si + + +Day 2; 04/17/25 + +- Polished the code +- Finished the parser +- Extracted the Scheduler to separate file +- Organized as a lib +- Created main script + + diff --git a/Ladygin/ConvTrack b/Ladygin/ConvTrack new file mode 100644 index 00000000..b9f43812 --- /dev/null +++ b/Ladygin/ConvTrack @@ -0,0 +1,45 @@ +#!/pscratch/sd/v/vladygin/doped-Si_project/MLFF_TDEP/testbench/bin/python + +import argparse + +# Custom dependences +from ConvergenceTracker import * + +def parser(): + """Parser of the arguments""" + parser = argparse.ArgumentParser(description='ConvTrack - tool for tracking kpoint convergence of point calcs') + parser.add_argument('-mode', type=str, default='qe', help='What software is used for simulation') + + # Arguments for the conv tracker + parser.add_argument('-workdir', type=str, default='./', help='Path to the working folder') + parser.add_argument('-target', type=str, default='total_energy', help='Target for error minimization') + parser.add_argument('-eps', type=float, default=1e-2, help='Convergence cryteria') + + + # Arguments for the driver + parser.add_argument('-input', type=str, default='pw.in', help='Input file with point calc settings') + parser.add_argument('-encut', type=float, default=40, help='Energy cutoff in Ry') + + + # Arguments for the scheduler + parser.add_argument('-k_sch', type=str, default='uniform', help='Which scheduler to use to obtain new kpoint') + parser.add_argument('-k_range', type=int, default= [2, 30, 2], nargs=3, help='k_start, k_end, k_step for uniform scheduler') + + args = parser.parse_args() + return args + +if __name__ == "__main__": + + args = parser() + + if args.k_sch == 'uniform': + k_sch = kpoint_scheduler_uniform(*args.k_sch) + + if args.mode == 'qe': + driver = DriverQE(args.workdir, args.input, args.encut) + tracker = ConvergenceTrackerQE(args.workdir, args.target, args.eps) + + + kpoint_opt, errors = tracker.find_opt(driver = driver, k_sch = k_sch) + + print(f"Optimal value of kpoint is {kpoint_opt} with error of {errors[-1]*1e3} meV") \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/__init__.py b/Ladygin/ConvergenceTracker/__init__.py index e69de29b..7e0dcd59 100644 --- a/Ladygin/ConvergenceTracker/__init__.py +++ b/Ladygin/ConvergenceTracker/__init__.py @@ -0,0 +1,6 @@ +from .driver import DriverQE +from .kpoint_scheduler import kpoint_scheduler_uniform +from .convergence_tracker_qe import ConvergenceTrackerQE + + +__all__ = [DriverQE, kpoint_scheduler_uniform, ConvergenceTrackerQE] diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_base.py b/Ladygin/ConvergenceTracker/convergence_tracker_base.py index bfea33cf..f5e28fef 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_base.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_base.py @@ -1,7 +1,7 @@ import pickle import os from driver import Driver - +from kpoint_scheduler import kpoint_scheduler_uniform class ConvergenceTracker(): """Base class for convergence tracker @@ -19,9 +19,19 @@ def __init__(self, workdir: str = './', target:str = 'total_energy', encut: floa self.encut = encut self.eps = eps self.kpoint_opt = None # optimal kpoint value + + def _step(self, k_curr: int) -> float: + """Step of the kpoint scheduler + + k_curr - current k point + """ + + def find_opt(self, driver: Driver, k_sch: kpoint_scheduler_uniform) -> None: + """Finds optimal kpoint by running simulations and refining the parameter until convergence reached - def find_opt(self, driver: Driver) -> None: - """Finds optimal kpoint by running simulations and refining the parameter until convergence reached""" + driver - drives for simulations + k_sch - kpoint generator + """ pass def save(self, name:str) -> None: diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py index 100e0d0d..3826fb83 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py @@ -1,59 +1,73 @@ from convergence_tracker_base import ConvergenceTracker from driver import DriverQE from utils import consts +from kpoint_scheduler import kpoint_scheduler_uniform class ConvergenceTrackerQE(ConvergenceTracker): """Implimentation of convergence tracker for Quantum Espresso Simulation""" - def __init__(self, workdir: str = './', target:str = 'total_energy', encut: float = 600., eps = 1e-2) -> None: + def __init__(self, workdir: str = './', target:str = 'total_energy', eps = 1e-2) -> None: """workdir - directory for input files and calculations target - target property to optimize encut - kinetic energy cutoff (in eV) eps - convergence criteria (in meV) """ super().__init__(workdir, target, encut, eps) + self.driver = DriverQE(workdir = self.workdir, + input_file_name = "pw.in", + encut = 40) + + + def _step(self, k_curr: int) -> float: + """Step of the kpoint scheduler + + k_curr - current k point + """ + self.driver.gen_input(k_curr) + self.driver.run() + return self.driver.extract_target(self.target) - def find_opt(self, driver: DriverQE = DriverQE()) -> None: + def find_opt(self, driver: DriverQE = None, k_sch: kpoint_scheduler_uniform = kpoint_scheduler_uniform(2, 30)) -> tuple: """Finds optimal kpoint by running simulations and refining the parameter until convergence reached DriverQE - calculation driver for quantum espresso """ - - # Start with 2 kpoints to estimate error - kpoint_list = [2, 4] - target_list = [] - - driver.gen_input(self.encut, kpoint_list[0]) - driver.run() - target_list.append(driver.extract_target(self.target)) - driver.gen_input(self.encut, kpoint_list[1]) - driver.run() - target_list.append(driver.extract_target(self.target)) + if type(driver) != type(None): + self.driver = driver + # Ensure working in same directory + try: + assert self.driver.workdir == self.workdir: + except AssertionError: + self.driver.workdir == self.workdir - error = [abs(target_list[-1] - target_list[-2])] + self.k_sch = k_sch - # while converge go increments of 2 - num_iter = 10 - i = 0 - while error[-1] > self.eps * 1e-3 and i < num_iter: - i+= 1 - - kpoint_list.append(kpoint_list[-1] + 2) - driver.gen_input(self.encut/consts['Ry'], kpoint_list[-1]) - driver.run() - target_list.append(driver.extract_target(self.target)) + # Start with 2 kpoints to estimate error + target_list = [float('inf')] + errors = [float('inf')] + k_curr = float('inf') + + while errors[-1] > self.eps * 1e-3 and k_curr > 0: + k_curr = k_sch.get_next() + target_curr = self._step(k_curr) + print(k_curr, target_curr) + target_list.append(target_curr) + errors.append(abs(target_list[-1] - target_list[-2])) - error.append(abs(target_list[-1] - target_list[-2])) + if errors[-1] < self.eps: + self.kpoint_opt = k_curr - if error[-1] < self.eps: - self.kpoint_opt = kpoint_list[-1] - + self.target_list = target_list[1:] + return k_curr, errors[2:] if __name__ == """__main__""": + + k_sch = kpoint_scheduler_uniform(2, 30) Tracker = ConvergenceTrackerQE('./', 'total_energy', 40*consts['Ry']) - - Tracker.find_opt() - - print(f"Optimal value of kpoint is {Tracker.kpoint_opt}") \ No newline at end of file + + kpoint_opt, errors = Tracker.find_opt(k_sch = k_sch) + + + print(f"Optimal value of kpoint is {kpoint_opt} with error of {errors[-1]*1e3} meV") \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/driver.py b/Ladygin/ConvergenceTracker/driver.py index 0711ff52..4cd841ee 100644 --- a/Ladygin/ConvergenceTracker/driver.py +++ b/Ladygin/ConvergenceTracker/driver.py @@ -1,19 +1,29 @@ from ase.io import read import subprocess as sp +from ase.io import espresso +import warnings +from exceptions import NofileWarning, MissingPseudoError, OutdirInconsistencyWarning +from utils import pw_template, atoms_template, parse_qe_input, convert_settings +import os class Driver(): """Base class for running simulation""" - def __init__(self, input_file: str) -> None: - """input_file - simulation settings""" - self.input_file = input_file + def __init__(self, workdir: str = './', input_file_name: str = 'pw.in', encut: float = 40) -> None: + """input_file - simulation settings input file name + workdir - working directory + encut - energy cutoff for point calc + """ + self.workdir = workdir + self.input_file_name = input_file_name + self.input_file = os.path.join(self.workdir, self.input_file_name) + self.encut = encut pass - def gen_input(self, encut: float, kpoint: float): + def gen_input(self, kpoint: float): """Generates the input file for the driver based on input parameters - encut - energy cutoff (in Ry) kpoint - kpoint dimension """ pass @@ -25,54 +35,60 @@ def run(self) -> None: def extract_target(self, target:str) -> float: """Extracts the target from the simulation output""" + pass class DriverQE(Driver): - def __init__(self, input_file: str = 'pw.in') -> None: - super().__init__(input_file) + def gen_input(self, kpoint: float) -> None: + """Generates the input file for the driver based on input parameters + + encut - energy cutoff (in Ry) + kpoint - kpoint dimension + """ + if not os.path.exists(self.input_file): + warnings.warn("The input file is missing I hope you know what you are doing. Calcs would be done silicon reference", NofileWarning) + atoms = atoms_template + settings = pw_template + else: + atoms = espresso.read_espresso_in(f'{self.workdir}/pw.in') + settings = parse_qe_input(f'{self.workdir}/pw.in') + + # Convert to ase format + print(settings) + settings_ase, pseudo = convert_settings(settings) + + for key in pseudo: + if not os.path.exists(os.path.join(self.workdir, "pseudo", pseudo[key])): + raise MissingPseudoError(f"Missing pseudo {pseudo[key]}, calculations will not run!") + + + try: + assert settings_ase['outdir'] == self.workdir and settings_ase['pseudo_dir'] == os.path.join(self.workdir, "pseudo") + except AssertionError: + warnings.warn("Output dir or pseudo_dir is inconsistant with working dir. Replacing outdir with working dir", OutdirInconsistencyWarning) + settings_ase['outdir'] = self.workdir + settings_ase['pseudo_dir'] = os.path.join(self.workdir, "pseudo") - def gen_input(self, encut: float, kpoint: float): + # Setting the new incut + settings_ase['encut'] = self.encut + # Saving updated pw.in with open(self.input_file, 'w') as f: - f.write(f""" &control - calculation = 'scf' - prefix = 'si' - restart_mode = 'from_scratch' - wf_collect = .true. - pseudo_dir = './' - outdir = './' - tprnfor = .true. - tstress = .true. - / - &system - ibrav = 2 - celldm(1) = 10.262 - nat = 2 - ntyp = 1 - ecutwfc = {encut} - nbnd = 8 - / - &electrons - diagonalization = 'david' - mixing_beta = 0.7 - conv_thr = 1.0d-13 - / - -ATOMIC_SPECIES - Si 28.0855 Si-PBE.upf -ATOMIC_POSITIONS crystal -Si 0.00 0.00 0.00 -Si -0.25 0.75 -0.25 -K_POINTS automatic -{kpoint} {kpoint} {kpoint} 0 0 0""") + espresso.write_espresso_in(f, atoms, input_data = settings_ase, kpts = (kpoint, kpoint, kpoint), pseudopotentials=pseudo) + def run(self) -> None: - - sp.run("srun -n 4 -c 2 /pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin/pw.x -nk 4 -input pw.in > pw.out", shell = True) + """Runs point simulation in the current folder + I use my own compiled quantum espresso for that + """ + out_file = os.path.join(self.workdir, f'{self.input_file_name.split(".")[0]}.out') + sp.run(f"/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin/pw.x -input {self.input_file} > {out_file}", shell = True) def extract_target(self, target:str) -> float: - data = read('pw.out') + """Extracts the target from the simulation output""" + out_file = os.path.join(self.workdir, f'{self.input_file_name.split(".")[0]}.out') + data = read(out_file) if target == 'total_energy': return data.get_total_energy() @@ -81,7 +97,7 @@ def extract_target(self, target:str) -> float: if __name__ == '__main__': driver = DriverQE() - driver.gen_input(40, 4) + driver.gen_input(4) driver.run() total_energy = driver.extract_target('total_energy') print("Total energy is equal to", total_energy, 'eV') diff --git a/Ladygin/ConvergenceTracker/exceptions.py b/Ladygin/ConvergenceTracker/exceptions.py new file mode 100644 index 00000000..cb895cab --- /dev/null +++ b/Ladygin/ConvergenceTracker/exceptions.py @@ -0,0 +1,11 @@ +class OutdirInconsistencyWarning(Warning): + """Warning for output dir is not working dir""" + pass + +class NofileWarning(Warning): + """Warning for absence of input setting file""" + pass + +class MissingPseudoError(Exception): + """Missing pseudo error""" + pass \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/kpoint_scheduler.py b/Ladygin/ConvergenceTracker/kpoint_scheduler.py new file mode 100644 index 00000000..20c341de --- /dev/null +++ b/Ladygin/ConvergenceTracker/kpoint_scheduler.py @@ -0,0 +1,44 @@ +class kpoint_scheduler(): + """Base class for updating kpoints using convergence search""" + + def __init__(self, k_start: int, k_end: int) -> None: + """Let's do seach on an interval [k_start, k_end] + + k_start - kpoint to start + k_end - kpoint to end + """ + + self.k_start = k_start + self.k_end = k_end + + def get_next(self, errors: list) -> int: + """A way to find next kpoint based on list of errors""" + pass + + + +class kpoint_scheduler_uniform(): + """Base class for updating kpoints using convergence search""" + + def __init__(self, k_start: int, k_end: int, k_step: int = 2) -> None: + """Let's do seach on an interval [k_start, k_end] + + k_start - kpoint to start + k_end - kpoint to end + + k_step - kpoint step + """ + super().__init__() + + self.k_step = k_step + self.k_list = range(k_start, k_end, k_step) + self.k_iter = iter(self.k_list) + + def get_next(self, errors: list = None) -> int: + """A way to find next kpoint based on list of errors""" + return next(self.k_iter, -1) + + + + + \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/utils.py b/Ladygin/ConvergenceTracker/utils.py index af1e4211..68ce1625 100644 --- a/Ladygin/ConvergenceTracker/utils.py +++ b/Ladygin/ConvergenceTracker/utils.py @@ -1 +1,123 @@ -consts = {'Ry': 13.6} \ No newline at end of file +from ase import Atoms +import os +# Some usefull consts +consts = {'Ry': 13.6} + +# Silicon template in case no pw.in +pw_template = {'control': {'calculation': 'scf', + 'prefix': 'si', + 'restart_mode': 'from_scratch', + 'wf_collect': '.true.', + 'pseudo_dir': './pseudo', + 'outdir': './', + 'tprnfor': '.true.', + 'tstress': '.true.'}, + 'system': {'ibrav': 2.0, + 'celldm(1)': 10.262, + 'nat': 2.0, + 'ntyp': 1.0, + 'ecutwfc': 40.0, + 'nbnd': 8.0}, + 'electrons': {'diagonalization': 'david', + 'mixing_beta': 0.7, + 'conv_thr': '1.0d-13'}, + 'ATOMIC_SPECIES': {'Si': '28.0855 Si-PBE.upf'}} + +atoms_template = Atoms(symbols='Si2', pbc=True, cell=[[-2.7152082560620205, 0.0, 2.7152082560620205], [0.0, 2.7152082560620205, 2.7152082560620205], [-2.7152082560620205, 2.7152082560620205, 0.0]], positions = [[0. , 0. , 0. ], [1.35760413, 1.35760413, 1.35760413]]) + + +def parse_qe_input(path: str) -> dict: + """ + Parse the input file of Quantum Espresso and return it as a dictionary + of the parameters. + + Args: + path (:obj:`str`): + Path to the Quantum Espresso input file. + + Returns: :obj:`dict` + """ + if not os.path.exists(path): + raise FileNotFoundError(f"File {path} does not exist!") + sections = [ + "ATOMIC_SPECIES", + "ATOMIC_POSITIONS", + "K_POINTS", + "ADDITIONAL_K_POINTS", + "CELL_PARAMETERS", + "CONSTRAINTS", + "OCCUPATIONS", + "ATOMIC_VELOCITIES", + "HUBBARD", + "ATOMIC_FORCES", + "SOLVENTS", + ] + with open(path, encoding="utf-8") as f: + lines = f.readlines() + params = {} + for raw_line in lines: + line = raw_line.strip().partition("#")[0] # ignore in-line comments + if line.startswith("&") or any(sec in line for sec in sections): + section = line.split()[0].replace("&", "") + params[section] = {} + elif line.startswith(("#", "!", "/")): + continue + elif "=" in line: + key, value = line.split("=") + # Convent numeric values to float + try: + value = float(value.strip()) + except Exception: + # string keywords in QE input file are enclosed in quotes + # so we remove them to avoid too many quotes when generating + # the input file with ase + value = value.strip() + value = value.replace("'", "") + value = value.replace('"', "") + + params[section][key.strip()] = value + elif len(line.split()) > 1: + key, value = line.split()[0], " ".join([str(val) for val in line.split()[1:]]) + params[section][key.strip()] = value + # Remove structure info (if present), as will be re-written with distorted structures + for section in [ + "ATOMIC_POSITIONS", + "K_POINTS", + "ADDITIONAL_K_POINTS", + "CELL_PARAMETERS", + ]: + params.pop(section, None) + if "SYSTEM" in params: + for key in ["celldm(1)", "nat", "ntyp", "ibrav"]: + params["SYSTEM"].pop(key, None) + return params + + +def convert_settings(settings:dict)->[dict, dict]: + """Converts qe settings to ase format + + returns settings and pseudo in ase format + """ + new_dict = {key: settings[tag][key] for tag in ['CONTROL', 'SYSTEM', 'ELECTRONS', 'control', 'system', 'electrons'] if tag in settings for key in settings[tag]} + + for key in new_dict: + # Fixing floats that should be ints + try: + if int(new_dict[key]) == new_dict[key]: + new_dict[key] = int(new_dict[key]) + except: + pass + # Fixing exponential format + try: + new_dict[key] = float(new_dict[key].replace('d', 'e')) + except: + pass + + for key in new_dict: + if new_dict[key] == '.true.': + new_dict[key] = True + if new_dict[key] == '.false.': + new_dict[key] = False + + pseudo = {key: settings["ATOMIC_SPECIES"][key].split()[1] for key in settings["ATOMIC_SPECIES"]} + return new_dict, pseudo \ No newline at end of file diff --git a/Ladygin/test_case/Si-PBE.upf b/Ladygin/test_case/pseudo/Si-PBE.upf similarity index 100% rename from Ladygin/test_case/Si-PBE.upf rename to Ladygin/test_case/pseudo/Si-PBE.upf diff --git a/Ladygin/test_case/pw.in b/Ladygin/test_case/pw.in index 1481d1f8..16e2c495 100644 --- a/Ladygin/test_case/pw.in +++ b/Ladygin/test_case/pw.in @@ -1,31 +1,42 @@ - &control - calculation = 'scf' - prefix = 'si' - restart_mode = 'from_scratch' - wf_collect = .true. - pseudo_dir = './' - outdir = './' - tprnfor = .true. - tstress = .true. - / - &system - ibrav = 2 - celldm(1) = 10.262 - nat = 2 - ntyp = 1 - ecutwfc = 40.0 - nbnd = 8 - / - &electrons - diagonalization = 'david' - mixing_beta = 0.7 - conv_thr = 1.0d-13 - / +&CONTROL + calculation = 'scf' + restart_mode = 'from_scratch' + wf_collect = .true. + tstress = .true. + tprnfor = .true. + outdir = './' + prefix = 'si' + pseudo_dir = './pseudo' +/ +&SYSTEM + nbnd = 8 + ecutwfc = 40 + ntyp = 1 + nat = 2 + ibrav = 0 +/ +&ELECTRONS + conv_thr = 1e-13 + mixing_beta = 0.7 + diagonalization = 'david' +/ +&IONS +/ +&CELL +/ ATOMIC_SPECIES - Si 28.0855 Si-PBE.upf -ATOMIC_POSITIONS crystal -Si 0.00 0.00 0.00 -Si -0.25 0.75 -0.25 +Si 28.085 Si-PBE.upf + K_POINTS automatic -18 18 18 0 0 0 \ No newline at end of file +18 18 18 0 0 0 + +CELL_PARAMETERS angstrom +-2.71520825606202 0.00000000000000 2.71520825606202 +0.00000000000000 2.71520825606202 2.71520825606202 +-2.71520825606202 2.71520825606202 0.00000000000000 + +ATOMIC_POSITIONS angstrom +Si 0.0000000000 0.0000000000 0.0000000000 +Si 1.3576041280 1.3576041280 1.3576041280 + diff --git a/Ladygin/test_case/run.sh b/Ladygin/test_case/run.sh index 35166c3e..168bf41e 100755 --- a/Ladygin/test_case/run.sh +++ b/Ladygin/test_case/run.sh @@ -16,8 +16,6 @@ module unload cudatoolkit/12.2 #module load nvhpc/23.1 module load cray-libsci/23.02.1.1 module load cray-hdf5-parallel -module load python/3.11 - module swap gpu cpu -python ../convergence_tracker_qe.py +python ../ConvergenceTracker/driver.py From 5082b249b15f784cfe4a1a0078d0ae159164d9f5 Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Sat, 19 Apr 2025 22:53:59 -0700 Subject: [PATCH 05/13] Some debugging and save options - Finished debugging lib and binary - Added stats save --- Ladygin/Changes.txt | 5 +- Ladygin/ConvTrack | 9 +-- Ladygin/ConvergenceTracker/__init__.py | 2 +- .../convergence_tracker_base.py | 14 +++-- .../convergence_tracker_qe.py | 63 ++++++++++++++----- Ladygin/ConvergenceTracker/driver.py | 5 +- Ladygin/ConvergenceTracker/exceptions.py | 4 ++ Ladygin/INSTALL | 5 ++ Ladygin/setup.py | 10 +++ Ladygin/test_case/run.sh | 2 +- 10 files changed, 87 insertions(+), 32 deletions(-) create mode 100644 Ladygin/INSTALL create mode 100644 Ladygin/setup.py diff --git a/Ladygin/Changes.txt b/Ladygin/Changes.txt index ca2f1d2d..a1314a79 100644 --- a/Ladygin/Changes.txt +++ b/Ladygin/Changes.txt @@ -4,7 +4,7 @@ Day 1; 04/17/25 - Tested on base case of Si -Day 2; 04/17/25 +Day 2; 04/18/25 - Polished the code - Finished the parser @@ -12,4 +12,7 @@ Day 2; 04/17/25 - Organized as a lib - Created main script +Day 3; 04/18/25 +- Finished debugging lib and binary +- Added stats save diff --git a/Ladygin/ConvTrack b/Ladygin/ConvTrack index b9f43812..6ff319d8 100644 --- a/Ladygin/ConvTrack +++ b/Ladygin/ConvTrack @@ -33,13 +33,14 @@ if __name__ == "__main__": args = parser() if args.k_sch == 'uniform': - k_sch = kpoint_scheduler_uniform(*args.k_sch) + k_sch = kpoint_scheduler_uniform(*args.k_range) if args.mode == 'qe': driver = DriverQE(args.workdir, args.input, args.encut) tracker = ConvergenceTrackerQE(args.workdir, args.target, args.eps) - kpoint_opt, errors = tracker.find_opt(driver = driver, k_sch = k_sch) - - print(f"Optimal value of kpoint is {kpoint_opt} with error of {errors[-1]*1e3} meV") \ No newline at end of file + k_list, target_list, errors = tracker.find_opt(driver = driver, k_sch = k_sch) + + + print(f"Optimal value of kpoint is {k_list[-1]} with error of {errors[-1]*1e3} meV") diff --git a/Ladygin/ConvergenceTracker/__init__.py b/Ladygin/ConvergenceTracker/__init__.py index 7e0dcd59..76bdd0dc 100644 --- a/Ladygin/ConvergenceTracker/__init__.py +++ b/Ladygin/ConvergenceTracker/__init__.py @@ -3,4 +3,4 @@ from .convergence_tracker_qe import ConvergenceTrackerQE -__all__ = [DriverQE, kpoint_scheduler_uniform, ConvergenceTrackerQE] +#__all__ = [DriverQE, kpoint_scheduler_uniform, ConvergenceTrackerQE] diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_base.py b/Ladygin/ConvergenceTracker/convergence_tracker_base.py index f5e28fef..da0fd437 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_base.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_base.py @@ -1,13 +1,13 @@ import pickle import os -from driver import Driver -from kpoint_scheduler import kpoint_scheduler_uniform +from .driver import Driver +from .kpoint_scheduler import kpoint_scheduler_uniform class ConvergenceTracker(): """Base class for convergence tracker Contains methods nessasure for any conv tracker""" - def __init__(self, workdir: str = './', target:str = 'total_energy', encut: float = 600., eps = 1e-2) -> None: + def __init__(self, workdir: str = './', target:str = 'total_energy', eps = 1e-2) -> None: """workdir - directory for input files and calculations target - target property to optimize encut - kinetic energy cutoff (in eV) @@ -16,16 +16,20 @@ def __init__(self, workdir: str = './', target:str = 'total_energy', encut: floa self.workdir = workdir self.target = target - self.encut = encut self.eps = eps self.kpoint_opt = None # optimal kpoint value + def save_stat(self, k_list: list, target_list: list, errors: list) -> None: + """Save stats of kpoint convergence iterations""" + pass + def _step(self, k_curr: int) -> float: """Step of the kpoint scheduler k_curr - current k point """ - + pass + def find_opt(self, driver: Driver, k_sch: kpoint_scheduler_uniform) -> None: """Finds optimal kpoint by running simulations and refining the parameter until convergence reached diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py index 3826fb83..a4429775 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py @@ -1,23 +1,38 @@ -from convergence_tracker_base import ConvergenceTracker -from driver import DriverQE -from utils import consts -from kpoint_scheduler import kpoint_scheduler_uniform +from .convergence_tracker_base import ConvergenceTracker +from .driver import DriverQE +from .utils import consts +from .kpoint_scheduler import kpoint_scheduler_uniform + +import os +import warnings +from .exceptions import NotConvergedWarning + class ConvergenceTrackerQE(ConvergenceTracker): """Implimentation of convergence tracker for Quantum Espresso Simulation""" - def __init__(self, workdir: str = './', target:str = 'total_energy', eps = 1e-2) -> None: + def __init__(self, workdir: str = './', target:str = 'total_energy', eps:float = 1e-2) -> None: """workdir - directory for input files and calculations target - target property to optimize encut - kinetic energy cutoff (in eV) eps - convergence criteria (in meV) """ - super().__init__(workdir, target, encut, eps) + super().__init__(workdir, target, eps) self.driver = DriverQE(workdir = self.workdir, input_file_name = "pw.in", encut = 40) + + def save_stat(self, k_list: list, target_list: list, errors: list) -> None: + """Save stats of kpoint convergence iterations""" + + out_path = os.path.join(self.workdir, 'stats.dat') + with open(out_path, 'w') as f: + f.write(f"kpoint {self.target} error\n") + for i in range(len(k_list)): + f.write('%d %.8f %.8f\n' % (k_list[i], target_list[i], errors[i])) + def _step(self, k_curr: int) -> float: """Step of the kpoint scheduler @@ -30,7 +45,11 @@ def _step(self, k_curr: int) -> float: def find_opt(self, driver: DriverQE = None, k_sch: kpoint_scheduler_uniform = kpoint_scheduler_uniform(2, 30)) -> tuple: """Finds optimal kpoint by running simulations and refining the parameter until convergence reached DriverQE - calculation driver for quantum espresso - + + Returns: + k_list - list of kpoints + target_list - list of target properties + errors - list of error in target properties """ if type(driver) != type(None): @@ -38,9 +57,9 @@ def find_opt(self, driver: DriverQE = None, k_sch: kpoint_scheduler_uniform = kp # Ensure working in same directory try: - assert self.driver.workdir == self.workdir: + assert self.driver.workdir == self.workdir except AssertionError: - self.driver.workdir == self.workdir + self.driver.workdir = self.workdir self.k_sch = k_sch @@ -48,26 +67,36 @@ def find_opt(self, driver: DriverQE = None, k_sch: kpoint_scheduler_uniform = kp target_list = [float('inf')] errors = [float('inf')] k_curr = float('inf') - + k_list = [] + + it = 1 + print(f"iter, k_curr, {self.target}, error") while errors[-1] > self.eps * 1e-3 and k_curr > 0: k_curr = k_sch.get_next() + k_list.append(k_curr) + target_curr = self._step(k_curr) - print(k_curr, target_curr) + target_list.append(target_curr) errors.append(abs(target_list[-1] - target_list[-2])) - if errors[-1] < self.eps: - self.kpoint_opt = k_curr + print("%d %d %.6f %.6f" % (it, k_curr, target_curr, errors[-1])) + it += 1 + + if errors[-1] > self.eps: + warnings.warn("The search procedure not converged. Result will return last kpoint and error", NotConvergedWarning) - self.target_list = target_list[1:] - return k_curr, errors[2:] + # Saving statistics + self.save_stat(k_list[1:], target_list[2:], errors[2:]) + + return k_list[1:], target_list[2:], errors[2:] if __name__ == """__main__""": k_sch = kpoint_scheduler_uniform(2, 30) Tracker = ConvergenceTrackerQE('./', 'total_energy', 40*consts['Ry']) - kpoint_opt, errors = Tracker.find_opt(k_sch = k_sch) + k_list, target_list, errors = Tracker.find_opt(k_sch = k_sch) - print(f"Optimal value of kpoint is {kpoint_opt} with error of {errors[-1]*1e3} meV") \ No newline at end of file + print(f"Optimal/Final value of kpoint is {k_list[-1]} with error of {errors[-1]*1e3} meV") \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/driver.py b/Ladygin/ConvergenceTracker/driver.py index 4cd841ee..89435798 100644 --- a/Ladygin/ConvergenceTracker/driver.py +++ b/Ladygin/ConvergenceTracker/driver.py @@ -2,8 +2,8 @@ import subprocess as sp from ase.io import espresso import warnings -from exceptions import NofileWarning, MissingPseudoError, OutdirInconsistencyWarning -from utils import pw_template, atoms_template, parse_qe_input, convert_settings +from .exceptions import NofileWarning, MissingPseudoError, OutdirInconsistencyWarning +from .utils import pw_template, atoms_template, parse_qe_input, convert_settings import os class Driver(): @@ -55,7 +55,6 @@ def gen_input(self, kpoint: float) -> None: settings = parse_qe_input(f'{self.workdir}/pw.in') # Convert to ase format - print(settings) settings_ase, pseudo = convert_settings(settings) for key in pseudo: diff --git a/Ladygin/ConvergenceTracker/exceptions.py b/Ladygin/ConvergenceTracker/exceptions.py index cb895cab..b7195533 100644 --- a/Ladygin/ConvergenceTracker/exceptions.py +++ b/Ladygin/ConvergenceTracker/exceptions.py @@ -6,6 +6,10 @@ class NofileWarning(Warning): """Warning for absence of input setting file""" pass +class NotConvergedWarning(Warning): + """Warning for kpoint search not being converged in the range of available kpoints""" + pass + class MissingPseudoError(Exception): """Missing pseudo error""" pass \ No newline at end of file diff --git a/Ladygin/INSTALL b/Ladygin/INSTALL new file mode 100644 index 00000000..16d04217 --- /dev/null +++ b/Ladygin/INSTALL @@ -0,0 +1,5 @@ +To install simply type in the main dir + +python setup.py install + +This will install lib and 'ConvTrack' binary file diff --git a/Ladygin/setup.py b/Ladygin/setup.py new file mode 100644 index 00000000..c325c18f --- /dev/null +++ b/Ladygin/setup.py @@ -0,0 +1,10 @@ +#!/usr/bin/env python + +from distutils.core import setup + +setup (name = 'ConvTrack', + version = '0.0.1', + description = 'Tracker of kpoint convergence', + packages=['ConvergenceTracker'], + scripts=['ConvTrack'], + install_requires=['ase']) \ No newline at end of file diff --git a/Ladygin/test_case/run.sh b/Ladygin/test_case/run.sh index 168bf41e..31430645 100755 --- a/Ladygin/test_case/run.sh +++ b/Ladygin/test_case/run.sh @@ -18,4 +18,4 @@ module load cray-libsci/23.02.1.1 module load cray-hdf5-parallel module swap gpu cpu -python ../ConvergenceTracker/driver.py +ConvTrack -workdir "./" -mode "qe" -target "total_energy" -eps 0.01 -input "pw.in" -encut 40 From 35cb6397661e7563b427facd3c5e67569d3c6f14 Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Sat, 19 Apr 2025 23:48:22 -0700 Subject: [PATCH 06/13] Final polish Day 3 - Separated job_runner into separate class - made remodelling of class inits --- Ladygin/Changes.txt | 2 ++ Ladygin/ConvTrack | 12 +++++-- Ladygin/ConvergenceTracker/__init__.py | 2 +- .../convergence_tracker_base.py | 2 +- .../convergence_tracker_qe.py | 6 ++-- Ladygin/ConvergenceTracker/driver.py | 32 ++++++++++++------- .../ConvergenceTracker/kpoint_scheduler.py | 7 ++-- 7 files changed, 42 insertions(+), 21 deletions(-) diff --git a/Ladygin/Changes.txt b/Ladygin/Changes.txt index a1314a79..e2a601a5 100644 --- a/Ladygin/Changes.txt +++ b/Ladygin/Changes.txt @@ -16,3 +16,5 @@ Day 3; 04/18/25 - Finished debugging lib and binary - Added stats save +- Separated job_runner into separate class +- made remodelling of class inits diff --git a/Ladygin/ConvTrack b/Ladygin/ConvTrack index 6ff319d8..df30aa91 100644 --- a/Ladygin/ConvTrack +++ b/Ladygin/ConvTrack @@ -8,7 +8,7 @@ from ConvergenceTracker import * def parser(): """Parser of the arguments""" parser = argparse.ArgumentParser(description='ConvTrack - tool for tracking kpoint convergence of point calcs') - parser.add_argument('-mode', type=str, default='qe', help='What software is used for simulation') + parser.add_argument('-mode', type=str, default='qe', help='What software is used for simulation', choices= ['qe', 'vasp']) # Arguments for the conv tracker parser.add_argument('-workdir', type=str, default='./', help='Path to the working folder') @@ -16,6 +16,11 @@ def parser(): parser.add_argument('-eps', type=float, default=1e-2, help='Convergence cryteria') + # Arguments for the calcutor + parser.add_argument('-mode_calc', type=str, default='shell', help='Which calculator to use (script or batch format will be supported)', choices= ['shell', 'job']) + parser.add_argument('-path_to_exec', type=str, default='/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin', help='Path to executables of the calculator') + parser.add_argument('-nk', type=int, default=1, help='Kpoint parralelization settings for quantum espresso') + # Arguments for the driver parser.add_argument('-input', type=str, default='pw.in', help='Input file with point calc settings') parser.add_argument('-encut', type=float, default=40, help='Energy cutoff in Ry') @@ -36,7 +41,10 @@ if __name__ == "__main__": k_sch = kpoint_scheduler_uniform(*args.k_range) if args.mode == 'qe': - driver = DriverQE(args.workdir, args.input, args.encut) + if args.mode_calc == 'shell': + calculator = job_runner_qe(args.path_to_exec, args.nk) + + driver = DriverQE(args.workdir, calculator, args.input, args.encut) tracker = ConvergenceTrackerQE(args.workdir, args.target, args.eps) diff --git a/Ladygin/ConvergenceTracker/__init__.py b/Ladygin/ConvergenceTracker/__init__.py index 76bdd0dc..a039606b 100644 --- a/Ladygin/ConvergenceTracker/__init__.py +++ b/Ladygin/ConvergenceTracker/__init__.py @@ -1,6 +1,6 @@ from .driver import DriverQE from .kpoint_scheduler import kpoint_scheduler_uniform from .convergence_tracker_qe import ConvergenceTrackerQE - +from .job_runner import job_runner_qe #__all__ = [DriverQE, kpoint_scheduler_uniform, ConvergenceTrackerQE] diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_base.py b/Ladygin/ConvergenceTracker/convergence_tracker_base.py index da0fd437..57e9852d 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_base.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_base.py @@ -7,7 +7,7 @@ class ConvergenceTracker(): """Base class for convergence tracker Contains methods nessasure for any conv tracker""" - def __init__(self, workdir: str = './', target:str = 'total_energy', eps = 1e-2) -> None: + def __init__(self, workdir: str, target:str, eps) -> None: """workdir - directory for input files and calculations target - target property to optimize encut - kinetic energy cutoff (in eV) diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py index a4429775..7e00c8b6 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py @@ -17,7 +17,9 @@ def __init__(self, workdir: str = './', target:str = 'total_energy', eps:float = encut - kinetic energy cutoff (in eV) eps - convergence criteria (in meV) """ - super().__init__(workdir, target, eps) + self.workdir = workdir + self.target = target + self.eps = eps self.driver = DriverQE(workdir = self.workdir, input_file_name = "pw.in", encut = 40) @@ -39,7 +41,7 @@ def _step(self, k_curr: int) -> float: k_curr - current k point """ self.driver.gen_input(k_curr) - self.driver.run() + self.driver.calc() return self.driver.extract_target(self.target) def find_opt(self, driver: DriverQE = None, k_sch: kpoint_scheduler_uniform = kpoint_scheduler_uniform(2, 30)) -> tuple: diff --git a/Ladygin/ConvergenceTracker/driver.py b/Ladygin/ConvergenceTracker/driver.py index 89435798..325d8cd5 100644 --- a/Ladygin/ConvergenceTracker/driver.py +++ b/Ladygin/ConvergenceTracker/driver.py @@ -1,23 +1,21 @@ from ase.io import read -import subprocess as sp from ase.io import espresso import warnings from .exceptions import NofileWarning, MissingPseudoError, OutdirInconsistencyWarning from .utils import pw_template, atoms_template, parse_qe_input, convert_settings +from .job_runner import job_runner_qe + import os class Driver(): """Base class for running simulation""" - def __init__(self, workdir: str = './', input_file_name: str = 'pw.in', encut: float = 40) -> None: + def __init__(self, workdir: str, calculator:job_runner_qe, input_file_name: str, encut: float) -> None: """input_file - simulation settings input file name workdir - working directory + calculator - job runner for point calculation encut - energy cutoff for point calc """ - self.workdir = workdir - self.input_file_name = input_file_name - self.input_file = os.path.join(self.workdir, self.input_file_name) - self.encut = encut pass @@ -28,7 +26,7 @@ def gen_input(self, kpoint: float): """ pass - def run(self) -> None: + def calc(self) -> None: """Runs point simulation in the current folder""" pass @@ -39,7 +37,19 @@ def extract_target(self, target:str) -> float: class DriverQE(Driver): + def __init__(self, workdir: str = './', calculator:job_runner_qe = job_runner_qe(), input_file_name: str = 'pw.in', encut: float = 40) -> None: + """input_file - simulation settings input file name + calculator - job runner for point calculation + workdir - working directory + encut - energy cutoff for point calc + """ + self.workdir = workdir + self.calculator = calculator + self.input_file_name = input_file_name + self.input_file = os.path.join(self.workdir, self.input_file_name) + self.encut = encut + def gen_input(self, kpoint: float) -> None: """Generates the input file for the driver based on input parameters @@ -77,12 +87,12 @@ def gen_input(self, kpoint: float) -> None: espresso.write_espresso_in(f, atoms, input_data = settings_ase, kpts = (kpoint, kpoint, kpoint), pseudopotentials=pseudo) - def run(self) -> None: + def calc(self) -> None: """Runs point simulation in the current folder I use my own compiled quantum espresso for that """ - out_file = os.path.join(self.workdir, f'{self.input_file_name.split(".")[0]}.out') - sp.run(f"/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin/pw.x -input {self.input_file} > {out_file}", shell = True) + output_file = os.path.join(self.workdir, f'{self.input_file_name.split(".")[0]}.out') + self.calculator.run_scf(self.input_file, output_file) def extract_target(self, target:str) -> float: """Extracts the target from the simulation output""" @@ -97,6 +107,6 @@ def extract_target(self, target:str) -> float: driver = DriverQE() driver.gen_input(4) - driver.run() + driver.calc() total_energy = driver.extract_target('total_energy') print("Total energy is equal to", total_energy, 'eV') diff --git a/Ladygin/ConvergenceTracker/kpoint_scheduler.py b/Ladygin/ConvergenceTracker/kpoint_scheduler.py index 20c341de..97c9ec1a 100644 --- a/Ladygin/ConvergenceTracker/kpoint_scheduler.py +++ b/Ladygin/ConvergenceTracker/kpoint_scheduler.py @@ -7,9 +7,7 @@ def __init__(self, k_start: int, k_end: int) -> None: k_start - kpoint to start k_end - kpoint to end """ - - self.k_start = k_start - self.k_end = k_end + pass def get_next(self, errors: list) -> int: """A way to find next kpoint based on list of errors""" @@ -28,7 +26,8 @@ def __init__(self, k_start: int, k_end: int, k_step: int = 2) -> None: k_step - kpoint step """ - super().__init__() + self.k_start = k_start + self.k_end = k_end self.k_step = k_step self.k_list = range(k_start, k_end, k_step) From b176326f62e27ec500cd4c4d17e67f84b2df4f8d Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Sun, 20 Apr 2025 21:02:01 -0700 Subject: [PATCH 07/13] - Added parrallelization option to the driver and job scheduler - Organized the lib in the modular fation - separated the metrix into the class --- Ladygin/Changes.txt | 6 ++ Ladygin/ConvTrack | 30 ++++++-- Ladygin/ConvergenceTracker/__init__.py | 4 +- .../convergence_tracker_base.py | 6 +- .../convergence_tracker_qe.py | 51 +++++++------ Ladygin/ConvergenceTracker/driver/__init__.py | 4 ++ .../driver/calculator/__init__.py | 2 + .../driver/calculator/calculator_base.py | 12 ++++ .../driver/calculator/calculator_qe.py | 71 +++++++++++++++++++ .../driver/calculator/utils.py | 20 ++++++ .../ConvergenceTracker/driver/driver_base.py | 30 ++++++++ .../{driver.py => driver/driver_qe.py} | 37 ++-------- .../ConvergenceTracker/kpoint_scheduler.py | 43 ----------- Ladygin/ConvergenceTracker/utils/consts.py | 2 + .../{ => utils}/exceptions.py | 0 .../{utils.py => utils/io.py} | 2 - Ladygin/setup.py | 3 +- 17 files changed, 213 insertions(+), 110 deletions(-) create mode 100644 Ladygin/ConvergenceTracker/driver/__init__.py create mode 100644 Ladygin/ConvergenceTracker/driver/calculator/__init__.py create mode 100644 Ladygin/ConvergenceTracker/driver/calculator/calculator_base.py create mode 100644 Ladygin/ConvergenceTracker/driver/calculator/calculator_qe.py create mode 100644 Ladygin/ConvergenceTracker/driver/calculator/utils.py create mode 100644 Ladygin/ConvergenceTracker/driver/driver_base.py rename Ladygin/ConvergenceTracker/{driver.py => driver/driver_qe.py} (71%) delete mode 100644 Ladygin/ConvergenceTracker/kpoint_scheduler.py create mode 100644 Ladygin/ConvergenceTracker/utils/consts.py rename Ladygin/ConvergenceTracker/{ => utils}/exceptions.py (100%) rename Ladygin/ConvergenceTracker/{utils.py => utils/io.py} (98%) diff --git a/Ladygin/Changes.txt b/Ladygin/Changes.txt index e2a601a5..4c2c51cd 100644 --- a/Ladygin/Changes.txt +++ b/Ladygin/Changes.txt @@ -18,3 +18,9 @@ Day 3; 04/18/25 - Added stats save - Separated job_runner into separate class - made remodelling of class inits + +Day 4: 04/19/25 + +- Added parrallelization option to the driver and job scheduler +- Organized the lib in the modular fation +- separated the metrix into the class diff --git a/Ladygin/ConvTrack b/Ladygin/ConvTrack index df30aa91..7938b3f2 100644 --- a/Ladygin/ConvTrack +++ b/Ladygin/ConvTrack @@ -16,9 +16,10 @@ def parser(): parser.add_argument('-eps', type=float, default=1e-2, help='Convergence cryteria') - # Arguments for the calcutor - parser.add_argument('-mode_calc', type=str, default='shell', help='Which calculator to use (script or batch format will be supported)', choices= ['shell', 'job']) + # Arguments for the calculator + parser.add_argument('-calc', type=str, default='shell', help='Which calculator to use (script or batch format will be supported)', choices= ['shell', 'par', 'job']) parser.add_argument('-path_to_exec', type=str, default='/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin', help='Path to executables of the calculator') + parser.add_argument('-ncores', type=int, default=1, help='Number of cores for parralel run') parser.add_argument('-nk', type=int, default=1, help='Kpoint parralelization settings for quantum espresso') # Arguments for the driver @@ -30,6 +31,9 @@ def parser(): parser.add_argument('-k_sch', type=str, default='uniform', help='Which scheduler to use to obtain new kpoint') parser.add_argument('-k_range', type=int, default= [2, 30, 2], nargs=3, help='k_start, k_end, k_step for uniform scheduler') + # Metrix mode + parser.add_argument('-metrix', type=str, default='mae', help='Which scheduler to use to obtain new kpoint') + args = parser.parse_args() return args @@ -40,15 +44,29 @@ if __name__ == "__main__": if args.k_sch == 'uniform': k_sch = kpoint_scheduler_uniform(*args.k_range) + if args.metrix == 'mae': + metrix = mae() + if args.mode == 'qe': - if args.mode_calc == 'shell': - calculator = job_runner_qe(args.path_to_exec, args.nk) + if args.calc == 'shell': + calculator = calculator_qe(args.path_to_exec) + if args.calc == 'par': + calculator = calculator_qe_par(args.path_to_exec, args.ncores, args.nk) + if args.calc == 'job': + calculator = calculator_qe_par(args.path_to_exec, args.ncores, args.nk, job = True, workdir = args.workdir) driver = DriverQE(args.workdir, calculator, args.input, args.encut) - tracker = ConvergenceTrackerQE(args.workdir, args.target, args.eps) - k_list, target_list, errors = tracker.find_opt(driver = driver, k_sch = k_sch) + tracker = ConvergenceTrackerQE(workdir = args.workdir, + target = args.target, + eps = args.eps, + driver = driver, + k_sch = k_sch, + metrix = metrix) + + + k_list, target_list, errors = tracker.find_opt() print(f"Optimal value of kpoint is {k_list[-1]} with error of {errors[-1]*1e3} meV") diff --git a/Ladygin/ConvergenceTracker/__init__.py b/Ladygin/ConvergenceTracker/__init__.py index a039606b..c2de3ef4 100644 --- a/Ladygin/ConvergenceTracker/__init__.py +++ b/Ladygin/ConvergenceTracker/__init__.py @@ -1,6 +1,6 @@ from .driver import DriverQE -from .kpoint_scheduler import kpoint_scheduler_uniform +from .driver import calculator_qe, calculator_qe_par +from .search import kpoint_scheduler_uniform, mae from .convergence_tracker_qe import ConvergenceTrackerQE -from .job_runner import job_runner_qe #__all__ = [DriverQE, kpoint_scheduler_uniform, ConvergenceTrackerQE] diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_base.py b/Ladygin/ConvergenceTracker/convergence_tracker_base.py index 57e9852d..eee24b54 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_base.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_base.py @@ -1,13 +1,13 @@ import pickle import os from .driver import Driver -from .kpoint_scheduler import kpoint_scheduler_uniform +from .search import kpoint_scheduler class ConvergenceTracker(): """Base class for convergence tracker Contains methods nessasure for any conv tracker""" - def __init__(self, workdir: str, target:str, eps) -> None: + def __init__(self, workdir: str, target:str, eps: float, driver: Driver) -> None: """workdir - directory for input files and calculations target - target property to optimize encut - kinetic energy cutoff (in eV) @@ -30,7 +30,7 @@ def _step(self, k_curr: int) -> float: """ pass - def find_opt(self, driver: Driver, k_sch: kpoint_scheduler_uniform) -> None: + def find_opt(self, driver: Driver, k_sch: kpoint_scheduler) -> None: """Finds optimal kpoint by running simulations and refining the parameter until convergence reached driver - drives for simulations diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py index 7e00c8b6..5569e52e 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py @@ -1,29 +1,38 @@ from .convergence_tracker_base import ConvergenceTracker -from .driver import DriverQE -from .utils import consts -from .kpoint_scheduler import kpoint_scheduler_uniform import os import warnings -from .exceptions import NotConvergedWarning +from .driver import DriverQE +from .search import kpoint_scheduler, kpoint_scheduler_uniform +from .search import metrix, mae + +from .utils.exceptions import NotConvergedWarning +from .utils.consts import consts class ConvergenceTrackerQE(ConvergenceTracker): """Implimentation of convergence tracker for Quantum Espresso Simulation""" - def __init__(self, workdir: str = './', target:str = 'total_energy', eps:float = 1e-2) -> None: + def __init__(self, workdir: str = './', target:str = 'total_energy', eps:float = 1e-2, + driver: DriverQE = DriverQE(), + k_sch: kpoint_scheduler = kpoint_scheduler_uniform(2, 30), + metrix: metrix = mae()) -> None: """workdir - directory for input files and calculations target - target property to optimize encut - kinetic energy cutoff (in eV) eps - convergence criteria (in meV) + + driver - driver for calculations + k_sch - kpoint scheduler for seach kpoint gen + metrix - metrix to compute an error """ self.workdir = workdir self.target = target self.eps = eps - self.driver = DriverQE(workdir = self.workdir, - input_file_name = "pw.in", - encut = 40) - + + self.driver = driver + self.k_sch = k_sch + self.metrix = metrix def save_stat(self, k_list: list, target_list: list, errors: list) -> None: @@ -44,27 +53,21 @@ def _step(self, k_curr: int) -> float: self.driver.calc() return self.driver.extract_target(self.target) - def find_opt(self, driver: DriverQE = None, k_sch: kpoint_scheduler_uniform = kpoint_scheduler_uniform(2, 30)) -> tuple: + def find_opt(self) -> tuple: """Finds optimal kpoint by running simulations and refining the parameter until convergence reached - DriverQE - calculation driver for quantum espresso - + Returns: k_list - list of kpoints target_list - list of target properties errors - list of error in target properties """ - if type(driver) != type(None): - self.driver = driver - # Ensure working in same directory try: assert self.driver.workdir == self.workdir except AssertionError: self.driver.workdir = self.workdir - self.k_sch = k_sch - # Start with 2 kpoints to estimate error target_list = [float('inf')] errors = [float('inf')] @@ -74,13 +77,13 @@ def find_opt(self, driver: DriverQE = None, k_sch: kpoint_scheduler_uniform = kp it = 1 print(f"iter, k_curr, {self.target}, error") while errors[-1] > self.eps * 1e-3 and k_curr > 0: - k_curr = k_sch.get_next() + k_curr = self.k_sch.get_next() k_list.append(k_curr) target_curr = self._step(k_curr) target_list.append(target_curr) - errors.append(abs(target_list[-1] - target_list[-2])) + errors.append(self.metrix(target_list[-1], target_list[-2])) print("%d %d %.6f %.6f" % (it, k_curr, target_curr, errors[-1])) it += 1 @@ -94,11 +97,15 @@ def find_opt(self, driver: DriverQE = None, k_sch: kpoint_scheduler_uniform = kp return k_list[1:], target_list[2:], errors[2:] if __name__ == """__main__""": + + driver = DriverQE(workdir = './', + input_file_name = "pw.in", + encut = 40) - k_sch = kpoint_scheduler_uniform(2, 30) - Tracker = ConvergenceTrackerQE('./', 'total_energy', 40*consts['Ry']) + Tracker = ConvergenceTrackerQE('./', 'total_energy', + driver = driver) - k_list, target_list, errors = Tracker.find_opt(k_sch = k_sch) + k_list, target_list, errors = Tracker.find_opt() print(f"Optimal/Final value of kpoint is {k_list[-1]} with error of {errors[-1]*1e3} meV") \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/driver/__init__.py b/Ladygin/ConvergenceTracker/driver/__init__.py new file mode 100644 index 00000000..910e0bab --- /dev/null +++ b/Ladygin/ConvergenceTracker/driver/__init__.py @@ -0,0 +1,4 @@ +from .driver_base import Driver +from .driver_qe import DriverQE + +from .calculator import calculator, calculator_qe, calculator_qe_par \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/driver/calculator/__init__.py b/Ladygin/ConvergenceTracker/driver/calculator/__init__.py new file mode 100644 index 00000000..acacde02 --- /dev/null +++ b/Ladygin/ConvergenceTracker/driver/calculator/__init__.py @@ -0,0 +1,2 @@ +from .calculator_base import calculator +from .calculator_qe import calculator_qe, calculator_qe_par diff --git a/Ladygin/ConvergenceTracker/driver/calculator/calculator_base.py b/Ladygin/ConvergenceTracker/driver/calculator/calculator_base.py new file mode 100644 index 00000000..61e44caf --- /dev/null +++ b/Ladygin/ConvergenceTracker/driver/calculator/calculator_base.py @@ -0,0 +1,12 @@ +class calculator(): + """Class for point calculations""" + + + def __init__(self, path_to_exec:str) -> None: + """path_to_exec - path to executable""" + pass + + + def run_scf(self, input_file:str, output_file:str) -> None: + """Runs scf calculation""" + pass diff --git a/Ladygin/ConvergenceTracker/driver/calculator/calculator_qe.py b/Ladygin/ConvergenceTracker/driver/calculator/calculator_qe.py new file mode 100644 index 00000000..622db619 --- /dev/null +++ b/Ladygin/ConvergenceTracker/driver/calculator/calculator_qe.py @@ -0,0 +1,71 @@ +import subprocess as sp +import os + +from .calculator_base import calculator +from .utils import wait_qe + +class calculator_qe(calculator): + """Class for point calculations using quantum espresso""" + + + def __init__(self, path_to_exec:str = "/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin") -> None: + """path_to_exec - path to executable""" + self.path_to_exec = path_to_exec + + + def run_scf(self, input_file:str, output_file:str) -> None: + """Runs scf calculations + + input_file - input settings + ouput_file - output file to save results + """ + binary = os.path.join(self.path_to_exec, "pw.x") + sp.run(f"{binary} -input {input_file} > {output_file}", shell = True) + +class calculator_qe_par(calculator_qe): + """Class for point calculations using quantum espresso using parralelization along q""" + + + def __init__(self, path_to_exec:str = "/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin", + ncores: int = 1, nk: int = 1, + job: bool = False, + workdir: str = None) -> None: + """path_to_exec - path to executable (kept for testing perposes better remove) + ncores - number of cores for parallelization + nk - quantum espresso parallelization setting along q + """ + self.path_to_exec = path_to_exec + + self.ncores = ncores + self.nk = nk + + self.job = job + if job: + if type(workdir) == type(None): + raise ValueError("For job calculation, one should provide a directory") + self.workdir = workdir + + def run_scf(self, input_file:str, output_file:str) -> None: + """Runs scf calculations + + input_file - input settings + ouput_file - output file to save results + job - submit job or run in shell + """ + binary = os.path.join(self.path_to_exec, "pw.x") + + if not self.job: + sp.run(f"srun -n {self.ncores} {binary} -nk {self.nk} -input {input_file} > {output_file}", shell = True) + else: + job_file = os.path.join(self.workdir, "calc.sh") + # Designed to run on one node + if self.ncores > 128: + raise ValueError("Number of cores is greater than number of cores per node") + + with open(f"{job_file}", "a") as file: + file.write(f"#!/bin/bash\n") + file.write(f"srun -n {self.ncores} {binary} -nk {self.nk} -input {input_file} > {output_file}\n") + + os.remove(output_file) + sp.run(f"sbatch -J qe_scf -q premium -C cpu -t 1:00:00 -N 1 -n {self.ncores} {job_file}", shell = True) + wait_qe(output_file) \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/driver/calculator/utils.py b/Ladygin/ConvergenceTracker/driver/calculator/utils.py new file mode 100644 index 00000000..58280bac --- /dev/null +++ b/Ladygin/ConvergenceTracker/driver/calculator/utils.py @@ -0,0 +1,20 @@ +import time +import os + +def wait_qe(output_file: str) -> None: + """Waits for QE calculation to finish" + + output_file: output file of point calc + """ + + finished = False + while not finished: + if os.path.exists(output_file): + with open(output_file, "r") as file: + lines = file.readlines() + + for line in reversed(lines): + if ("JOB DONE." in line): + finished = True + break + time.sleep(5) \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/driver/driver_base.py b/Ladygin/ConvergenceTracker/driver/driver_base.py new file mode 100644 index 00000000..e498cbf1 --- /dev/null +++ b/Ladygin/ConvergenceTracker/driver/driver_base.py @@ -0,0 +1,30 @@ +from .calculator import calculator + + +class Driver(): + """Base class for running simulation""" + + def __init__(self, workdir: str, calculator:calculator, input_file_name: str, encut: float) -> None: + """input_file - simulation settings input file name + workdir - working directory + calculator - job runner for point calculation + encut - energy cutoff for point calc + """ + pass + + + def gen_input(self, kpoint: float): + """Generates the input file for the driver based on input parameters + + kpoint - kpoint dimension + """ + pass + + def calc(self) -> None: + """Runs point simulation in the current folder""" + pass + + + def extract_target(self, target:str) -> float: + """Extracts the target from the simulation output""" + pass diff --git a/Ladygin/ConvergenceTracker/driver.py b/Ladygin/ConvergenceTracker/driver/driver_qe.py similarity index 71% rename from Ladygin/ConvergenceTracker/driver.py rename to Ladygin/ConvergenceTracker/driver/driver_qe.py index 325d8cd5..1dab09c5 100644 --- a/Ladygin/ConvergenceTracker/driver.py +++ b/Ladygin/ConvergenceTracker/driver/driver_qe.py @@ -1,43 +1,18 @@ from ase.io import read from ase.io import espresso import warnings -from .exceptions import NofileWarning, MissingPseudoError, OutdirInconsistencyWarning -from .utils import pw_template, atoms_template, parse_qe_input, convert_settings -from .job_runner import job_runner_qe - import os -class Driver(): - """Base class for running simulation""" - - def __init__(self, workdir: str, calculator:job_runner_qe, input_file_name: str, encut: float) -> None: - """input_file - simulation settings input file name - workdir - working directory - calculator - job runner for point calculation - encut - energy cutoff for point calc - """ - pass +from ..utils import NofileWarning, MissingPseudoError, OutdirInconsistencyWarning +from ..utils import pw_template, atoms_template, parse_qe_input, convert_settings +from .calculator import calculator_qe - def gen_input(self, kpoint: float): - """Generates the input file for the driver based on input parameters - - kpoint - kpoint dimension - """ - pass - - def calc(self) -> None: - """Runs point simulation in the current folder""" - pass - - - def extract_target(self, target:str) -> float: - """Extracts the target from the simulation output""" - pass +from .driver_base import Driver class DriverQE(Driver): - def __init__(self, workdir: str = './', calculator:job_runner_qe = job_runner_qe(), input_file_name: str = 'pw.in', encut: float = 40) -> None: + def __init__(self, workdir: str = './', calculator:calculator_qe = calculator_qe(), input_file_name: str = 'pw.in', encut: float = 40) -> None: """input_file - simulation settings input file name calculator - job runner for point calculation workdir - working directory @@ -109,4 +84,4 @@ def extract_target(self, target:str) -> float: driver.gen_input(4) driver.calc() total_energy = driver.extract_target('total_energy') - print("Total energy is equal to", total_energy, 'eV') + print("Total energy is equal to", total_energy, 'eV') \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/kpoint_scheduler.py b/Ladygin/ConvergenceTracker/kpoint_scheduler.py deleted file mode 100644 index 97c9ec1a..00000000 --- a/Ladygin/ConvergenceTracker/kpoint_scheduler.py +++ /dev/null @@ -1,43 +0,0 @@ -class kpoint_scheduler(): - """Base class for updating kpoints using convergence search""" - - def __init__(self, k_start: int, k_end: int) -> None: - """Let's do seach on an interval [k_start, k_end] - - k_start - kpoint to start - k_end - kpoint to end - """ - pass - - def get_next(self, errors: list) -> int: - """A way to find next kpoint based on list of errors""" - pass - - - -class kpoint_scheduler_uniform(): - """Base class for updating kpoints using convergence search""" - - def __init__(self, k_start: int, k_end: int, k_step: int = 2) -> None: - """Let's do seach on an interval [k_start, k_end] - - k_start - kpoint to start - k_end - kpoint to end - - k_step - kpoint step - """ - self.k_start = k_start - self.k_end = k_end - - self.k_step = k_step - self.k_list = range(k_start, k_end, k_step) - self.k_iter = iter(self.k_list) - - def get_next(self, errors: list = None) -> int: - """A way to find next kpoint based on list of errors""" - return next(self.k_iter, -1) - - - - - \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/utils/consts.py b/Ladygin/ConvergenceTracker/utils/consts.py new file mode 100644 index 00000000..1c3c86a3 --- /dev/null +++ b/Ladygin/ConvergenceTracker/utils/consts.py @@ -0,0 +1,2 @@ +# Some usefull consts +consts = {'Ry': 13.6} \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/exceptions.py b/Ladygin/ConvergenceTracker/utils/exceptions.py similarity index 100% rename from Ladygin/ConvergenceTracker/exceptions.py rename to Ladygin/ConvergenceTracker/utils/exceptions.py diff --git a/Ladygin/ConvergenceTracker/utils.py b/Ladygin/ConvergenceTracker/utils/io.py similarity index 98% rename from Ladygin/ConvergenceTracker/utils.py rename to Ladygin/ConvergenceTracker/utils/io.py index 68ce1625..1fe1abfd 100644 --- a/Ladygin/ConvergenceTracker/utils.py +++ b/Ladygin/ConvergenceTracker/utils/io.py @@ -1,7 +1,5 @@ from ase import Atoms import os -# Some usefull consts -consts = {'Ry': 13.6} # Silicon template in case no pw.in pw_template = {'control': {'calculation': 'scf', diff --git a/Ladygin/setup.py b/Ladygin/setup.py index c325c18f..567aefc3 100644 --- a/Ladygin/setup.py +++ b/Ladygin/setup.py @@ -5,6 +5,7 @@ setup (name = 'ConvTrack', version = '0.0.1', description = 'Tracker of kpoint convergence', - packages=['ConvergenceTracker'], + packages=['ConvergenceTracker', 'ConvergenceTracker.driver', 'ConvergenceTracker.driver.calculator', + 'ConvergenceTracker.search', 'ConvergenceTracker.utils'], scripts=['ConvTrack'], install_requires=['ase']) \ No newline at end of file From 64567a6283bf60d48c975686635f8afcf30b4951 Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Sun, 20 Apr 2025 22:50:24 -0700 Subject: [PATCH 08/13] - Started computing for other systems - Made summery notebook --- .../convergence_tracker_qe.py | 6 +- .../ConvergenceTracker/driver/driver_qe.py | 2 +- .../search/kpoint_scheduler.py | 43 + Ladygin/ConvergenceTracker/search/metrix.py | 17 + Ladygin/examples/Convergence_summery.ipynb | 290 + .../Al/pseudo/Al.pbe-n-rrkjus_psl.1.0.0.UPF | 16408 +++++++ Ladygin/examples/inputs/Al/pw.in | 32 + .../.ipynb_checkpoints/pw-checkpoint.in | 35 + Ladygin/examples/inputs/CsPbBr3/pseudo/Br.upf | 20657 ++++++++ Ladygin/examples/inputs/CsPbBr3/pseudo/Cs.upf | 39658 ++++++++++++++++ Ladygin/examples/inputs/CsPbBr3/pseudo/Pb.upf | 24057 ++++++++++ Ladygin/examples/inputs/CsPbBr3/pw.in | 35 + .../MgB2/.ipynb_checkpoints/pw-checkpoint.in | 32 + .../examples/inputs/MgB2/pseudo/B.pz-vbc.UPF | 334 + .../inputs/MgB2/pseudo/Mg.pz-n-vbc.UPF | 444 + Ladygin/examples/inputs/MgB2/pw.in | 32 + Ladygin/examples/inputs/Si/pseudo/Si-PBE.upf | 6765 +++ Ladygin/examples/inputs/Si/pw.in | 41 + Ladygin/examples/run.sh | 27 + 19 files changed, 108912 insertions(+), 3 deletions(-) create mode 100644 Ladygin/ConvergenceTracker/search/kpoint_scheduler.py create mode 100644 Ladygin/ConvergenceTracker/search/metrix.py create mode 100644 Ladygin/examples/Convergence_summery.ipynb create mode 100644 Ladygin/examples/inputs/Al/pseudo/Al.pbe-n-rrkjus_psl.1.0.0.UPF create mode 100644 Ladygin/examples/inputs/Al/pw.in create mode 100644 Ladygin/examples/inputs/CsPbBr3/.ipynb_checkpoints/pw-checkpoint.in create mode 100644 Ladygin/examples/inputs/CsPbBr3/pseudo/Br.upf create mode 100644 Ladygin/examples/inputs/CsPbBr3/pseudo/Cs.upf create mode 100644 Ladygin/examples/inputs/CsPbBr3/pseudo/Pb.upf create mode 100644 Ladygin/examples/inputs/CsPbBr3/pw.in create mode 100644 Ladygin/examples/inputs/MgB2/.ipynb_checkpoints/pw-checkpoint.in create mode 100644 Ladygin/examples/inputs/MgB2/pseudo/B.pz-vbc.UPF create mode 100644 Ladygin/examples/inputs/MgB2/pseudo/Mg.pz-n-vbc.UPF create mode 100644 Ladygin/examples/inputs/MgB2/pw.in create mode 100644 Ladygin/examples/inputs/Si/pseudo/Si-PBE.upf create mode 100644 Ladygin/examples/inputs/Si/pw.in create mode 100755 Ladygin/examples/run.sh diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py index 5569e52e..ecd6526a 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py @@ -76,8 +76,10 @@ def find_opt(self) -> tuple: it = 1 print(f"iter, k_curr, {self.target}, error") - while errors[-1] > self.eps * 1e-3 and k_curr > 0: + while errors[-1] > self.eps * 1e-3: k_curr = self.k_sch.get_next() + if k_curr < 0: + break k_list.append(k_curr) target_curr = self._step(k_curr) @@ -88,7 +90,7 @@ def find_opt(self) -> tuple: print("%d %d %.6f %.6f" % (it, k_curr, target_curr, errors[-1])) it += 1 - if errors[-1] > self.eps: + if errors[-1] > self.eps * 1e-3: warnings.warn("The search procedure not converged. Result will return last kpoint and error", NotConvergedWarning) # Saving statistics diff --git a/Ladygin/ConvergenceTracker/driver/driver_qe.py b/Ladygin/ConvergenceTracker/driver/driver_qe.py index 1dab09c5..0d631ca2 100644 --- a/Ladygin/ConvergenceTracker/driver/driver_qe.py +++ b/Ladygin/ConvergenceTracker/driver/driver_qe.py @@ -55,7 +55,7 @@ def gen_input(self, kpoint: float) -> None: settings_ase['pseudo_dir'] = os.path.join(self.workdir, "pseudo") # Setting the new incut - settings_ase['encut'] = self.encut + settings_ase['ecutwfc'] = self.encut # Saving updated pw.in with open(self.input_file, 'w') as f: diff --git a/Ladygin/ConvergenceTracker/search/kpoint_scheduler.py b/Ladygin/ConvergenceTracker/search/kpoint_scheduler.py new file mode 100644 index 00000000..d2a78119 --- /dev/null +++ b/Ladygin/ConvergenceTracker/search/kpoint_scheduler.py @@ -0,0 +1,43 @@ +class kpoint_scheduler(): + """Base class for updating kpoints using convergence search""" + + def __init__(self, k_start: int, k_end: int) -> None: + """Let's do seach on an interval [k_start, k_end] + + k_start - kpoint to start + k_end - kpoint to end + """ + pass + + def get_next(self, errors: list) -> int: + """A way to find next kpoint based on list of errors""" + pass + + + +class kpoint_scheduler_uniform(kpoint_scheduler): + """Base class for updating kpoints using convergence search""" + + def __init__(self, k_start: int, k_end: int, k_step: int = 2) -> None: + """Let's do seach on an interval [k_start, k_end] + + k_start - kpoint to start + k_end - kpoint to end + + k_step - kpoint step + """ + self.k_start = k_start + self.k_end = k_end + + self.k_step = k_step + self.k_list = range(k_start, k_end, k_step) + self.k_iter = iter(self.k_list) + + def get_next(self, errors: list = None) -> int: + """A way to find next kpoint based on list of errors""" + return next(self.k_iter, -1) + + + + + \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/search/metrix.py b/Ladygin/ConvergenceTracker/search/metrix.py new file mode 100644 index 00000000..a053c7bb --- /dev/null +++ b/Ladygin/ConvergenceTracker/search/metrix.py @@ -0,0 +1,17 @@ +class metrix(): + """Measures error of convergence testing""" + + def __init__(self): + pass + + def __call__(self, value_1: float, value_2: float) -> float: + """Measures error between two points""" + pass + + +class mae(metrix): + """Mean absolute error for convergence testing""" + + def __call__(self, value_1: float, value_2: float) -> float: + """Measures error between two points as mean absolute error""" + return abs(value_1 - value_2) \ No newline at end of file diff --git a/Ladygin/examples/Convergence_summery.ipynb b/Ladygin/examples/Convergence_summery.ipynb new file mode 100644 index 00000000..70106ea0 --- /dev/null +++ b/Ladygin/examples/Convergence_summery.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "03da9500-510b-448e-a814-a486c4d749ec", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "010d20ff-c064-4bc6-8b05-59b7f0f4ed93", + "metadata": {}, + "source": [ + "### 1. Al (1 atom)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d356cc0c-d0e1-4a44-b2a6-7f5f97706610", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 60.0)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"Al\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 60)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], data[:, 2]*1e3)\n", + "\n", + "ax2.set_ylabel(\"Error (meV)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 60)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "210c3d4c-127f-4d83-9be7-9c88f44b5f89", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value for Al of kpoint grid is 58x58x58 with error of 0.0098 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value for Al of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "markdown", + "id": "d7b38784-c1ac-4287-92af-2028fc9b9bca", + "metadata": {}, + "source": [ + "### 2. Si (2 atoms)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f7bf0e0f-bbe4-4cc9-b7c0-ec0b27a07f40", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 60.0)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"Si\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 60)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], data[:, 2]*1e3)\n", + "\n", + "ax2.set_ylabel(\"Error (meV)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 60)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "92ca6d39-01ec-4f8b-8b2e-2ef63dd82e91", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 22x22x22 with error of 0.00218 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "markdown", + "id": "dac36942-3049-4c52-b9b7-a6c1a0c8b2ef", + "metadata": {}, + "source": [ + "### 3. MgB2 (3 atoms)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "6755b7c5-1747-4b7c-bdb5-89f722208ce9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 60.0)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"MgB2\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 60)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], data[:, 2]*1e3)\n", + "\n", + "ax2.set_ylabel(\"Error (meV)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 60)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a36f163b-9d55-4f1e-ba82-0400d557cac3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 26x26x26 with error of 0.00177 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2374fec5-d5f2-44f7-9e36-adfce75bd96b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "testbench", + "language": "python", + "name": "testbench" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Ladygin/examples/inputs/Al/pseudo/Al.pbe-n-rrkjus_psl.1.0.0.UPF b/Ladygin/examples/inputs/Al/pseudo/Al.pbe-n-rrkjus_psl.1.0.0.UPF new file mode 100644 index 00000000..30da4418 --- /dev/null +++ b/Ladygin/examples/inputs/Al/pseudo/Al.pbe-n-rrkjus_psl.1.0.0.UPF @@ -0,0 +1,16408 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.3 +Author: ADC +Generation date: 4Sep2018 +Pseudopotential type: USPP +Element: Al +Functional: PBE + Suggested minimum cutoff for wavefunctions: 29. Ry + Suggested minimum cutoff for charge density: 143. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.9000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 3S 1 0 2.00 1.700 1.900 -0.569826 + 3P 2 1 1.00 1.700 2.000 -0.199338 + Generation configuration: + 3S 1 0 2.00 1.700 1.900 -0.569824 + 3S 1 0 0.00 1.700 1.900 6.000000 + 3P 2 1 1.00 1.700 2.000 -0.199338 + 3P 2 1 0.00 1.700 2.000 3.500000 + 3D 3 2 0.00 1.700 1.900 0.100000 + 3D 3 2 0.00 1.700 1.900 0.300000 + Pseudization used: troullier-martins + + + &input + title='Al', + zed=13., + rel=1, + config='[Ne] 3s2 3p1 3d-1', + iswitch=3, + dft='PBE' + / + &inputp + lpaw=.false., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='Al.pbe-n-rrkjus_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=1.9, + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., + new_core_ps=.true., + rcore=1.8, + tm=.true. + / +6 +3S 1 0 2.00 0.00 1.70 1.90 0.0 +3S 1 0 0.00 6.00 1.70 1.90 0.0 +3P 2 1 1.00 0.00 1.70 2.00 0.0 +3P 2 1 0.00 3.50 1.70 2.00 0.0 +3D 3 2 0.00 0.10 1.70 1.90 0.0 +3D 3 2 0.00 0.30 1.70 1.90 0.0 + + + + + + + 7.014476658111662e-5 7.102707912838633e-5 7.192048980127554e-5 7.282513819701953e-5 + 7.374116566877076e-5 7.466871534768530e-5 7.560793216528758e-5 7.655896287611625e-5 + 7.752195608065464e-5 7.849706224855035e-5 7.948443374212593e-5 8.048422484018605e-5 + 8.149659176212387e-5 8.252169269233052e-5 8.355968780491215e-5 8.461073928871679e-5 + 8.567501137267717e-5 8.675267035147172e-5 8.784388461150826e-5 8.894882465723521e-5 + 9.006766313778265e-5 9.120057487393945e-5 9.234773688546928e-5 9.350932841877036e-5 + 9.468553097488316e-5 9.587652833784997e-5 9.708250660343188e-5 9.830365420818650e-5 + 9.954016195891139e-5 1.007922230624585e-4 1.020600331559224e-4 1.033437903372098e-4 + 1.046436951959918e-4 1.059599508450475e-4 1.072927629519999e-4 1.086423397714524e-4 + 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-2.090590524860620e-10 -2.090475965848882e-10 + -2.090630801092902e-10 -2.090782789215319e-10 -2.090670919508585e-10 -2.090749306426055e-10 + -2.090680016943978e-10 -2.090736717840528e-10 -2.090795701716639e-10 -2.090699999682492e-10 + -2.090794742212591e-10 -2.090540424292126e-10 -2.090625273961851e-10 + + + + diff --git a/Ladygin/examples/inputs/Al/pw.in b/Ladygin/examples/inputs/Al/pw.in new file mode 100644 index 00000000..44172e49 --- /dev/null +++ b/Ladygin/examples/inputs/Al/pw.in @@ -0,0 +1,32 @@ +&CONTROL + calculation= 'scf' + restart_mode= 'from_scratch' + prefix= 'al' + outdir= '/tmp/' + pseudo_dir= '../pseudos/' +/ + +&SYSTEM + ibrav= 2 + celldm(1) = 7.63075 + nat= 1 + ntyp= 1 + ecutwfc= 50 + ecutrho= 500 + occupations= 'smearing' + smearing= 'gaussian' + degauss= 0.01 +/ + +&ELECTRONS + conv_thr= 1e-13 +/ + +ATOMIC_SPECIES + Al 26.981539 Al.pbe-n-rrkjus_psl.1.0.0.UPF + +ATOMIC_POSITIONS (alat) + Al 0.00 0.00 0.00 + +K_POINTS (automatic) + 10 10 10 0 0 0 diff --git a/Ladygin/examples/inputs/CsPbBr3/.ipynb_checkpoints/pw-checkpoint.in b/Ladygin/examples/inputs/CsPbBr3/.ipynb_checkpoints/pw-checkpoint.in new file mode 100644 index 00000000..23f36dca --- /dev/null +++ b/Ladygin/examples/inputs/CsPbBr3/.ipynb_checkpoints/pw-checkpoint.in @@ -0,0 +1,35 @@ +&control + calculation='scf' + restart_mode='from_scratch' + prefix = 'scf' + outdir='./' + pseudo_dir='./' + verbosity='high' + tstress=.true. + tprnfor=.true. +/ +&system + ibrav=1 + nat=5 + ntyp=3 + ecutwfc= 40 + A = 5.874 +/ +&electrons + diagonalization='david' + mixing_mode='plain' + mixing_beta=0.7 + conv_thr=1e-13 +/ +ATOMIC_SPECIES +Cs 132.90545 Cs.upf +Pb 207.2 Pb.upf +Br 79.904 Br.upf +K_POINTS {automatic} +4 4 4 0 0 0 +ATOMIC_POSITIONS (crystal) + Cs 0.500000 0.500000 0.500000 + Pb 0.000000 0.000000 0.000000 + Br 0.000000 0.000000 0.500000 + Br 0.000000 0.500000 0.000000 + Br 0.500000 0.000000 0.000000 diff --git a/Ladygin/examples/inputs/CsPbBr3/pseudo/Br.upf b/Ladygin/examples/inputs/CsPbBr3/pseudo/Br.upf new file mode 100644 index 00000000..526789f4 --- /dev/null +++ b/Ladygin/examples/inputs/CsPbBr3/pseudo/Br.upf @@ -0,0 +1,20657 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.3 +Author: ADC +Generation date: 6Sep2018 +Pseudopotential type: PAW +Element: Br +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 56. Ry + Suggested minimum cutoff for charge density: 289. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.9000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 4S 1 0 2.00 1.300 1.700 -1.479842 + 4P 2 1 5.00 1.400 1.700 -0.577675 + 3D 3 2 10.00 1.300 1.500 -4.903714 + Generation configuration: + 4S 1 0 2.00 1.300 1.700 -1.479837 + 4S 1 0 0.00 1.300 1.700 6.000000 + 4P 2 1 5.00 1.400 1.700 -0.577671 + 4P 2 1 0.00 1.400 1.700 6.000000 + 3D 3 2 10.00 1.300 1.500 -4.903712 + 3D 3 2 0.00 1.300 1.500 0.050000 + Pseudization used: troullier-martins + + + &input + title='Br', + zed=35., + rel=1, + config='[Ar] 4s2 4p5 3d10', + iswitch=3, + dft='PBE' + / + &inputp + lpaw = .true., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='Br.pbe-dn-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=1.9, + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., + 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Dal Corso v.6.4.1 +Author: ADC +Generation date: 6Jun2019 +Pseudopotential type: PAW +Element: Cs +Functional: SLA PW PSX PSC + Suggested minimum cutoff for wavefunctions: 34. Ry + Suggested minimum cutoff for charge density: 258. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 2.0000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 5S 1 0 2.00 1.600 2.100 -1.968593 + 6S 2 0 1.00 1.600 2.100 -0.152875 + 5P 2 1 6.00 1.600 2.300 -0.995261 + Generation configuration: + 5S 1 0 2.00 1.600 2.100 -1.968589 + 6S 2 0 1.00 1.600 2.100 -0.152876 + 6S 2 0 0.00 1.600 2.100 1.000000 + 5P 2 1 6.00 1.600 2.300 -0.995258 + 6P 3 1 0.00 1.600 2.300 6.000000 + 5D 3 2 0.00 1.500 2.100 0.300000 + 5D 3 2 0.00 1.500 2.100 4.050000 + 4F 4 3 0.00 1.500 2.100 0.050000 + 4F 4 3 0.00 1.500 2.100 4.050000 + Pseudization used: troullier-martins + + + &input + title='Cs', + zed=55., + rel=1, + config='[Xe] 6s1 6p0 5d-1 4f-1', + iswitch=3, + dft='PBESOL' + / + &inputp + lpaw = .true., + use_xsd=.FALSE., + pseudotype=3, + 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0.000000 0.000000 0.500000 + Br 0.000000 0.500000 0.000000 + Br 0.500000 0.000000 0.000000 diff --git a/Ladygin/examples/inputs/MgB2/.ipynb_checkpoints/pw-checkpoint.in b/Ladygin/examples/inputs/MgB2/.ipynb_checkpoints/pw-checkpoint.in new file mode 100644 index 00000000..3176f867 --- /dev/null +++ b/Ladygin/examples/inputs/MgB2/.ipynb_checkpoints/pw-checkpoint.in @@ -0,0 +1,32 @@ +&control + calculation = 'scf' + restart_mode = 'from_scratch' + prefix = 'mgb2' + pseudo_dir = './' + outdir = './' +/ +&system + ibrav = 4 + celldm(1) = 5.8260252227888 + celldm(3) = 1.1420694129095 + nat = 3 + ntyp = 2 + ecutwfc = 40 + smearing = 'fermi-dirac' + occupations = 'smearing' + degauss = 0.02 +/ +&electrons + diagonalization = 'david' + mixing_beta = 0.7 + conv_thr = 1.0d-12 +/ +ATOMIC_SPECIES + Mg 24.305 Mg.pz-n-vbc.UPF + B 10.811 B.pz-vbc.UPF +ATOMIC_POSITIONS crystal + Mg 0.000000000 0.000000000 0.000000000 + B 0.333333333 0.666666667 0.500000000 + B 0.666666667 0.333333333 0.500000000 +K_POINTS AUTOMATIC + 2 2 2 0 0 0 diff --git a/Ladygin/examples/inputs/MgB2/pseudo/B.pz-vbc.UPF b/Ladygin/examples/inputs/MgB2/pseudo/B.pz-vbc.UPF new file mode 100644 index 00000000..41a4ad27 --- /dev/null +++ b/Ladygin/examples/inputs/MgB2/pseudo/B.pz-vbc.UPF @@ -0,0 +1,334 @@ + +Generated using ld1 code +Author: P. Giannozzi (?) 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a/Ladygin/examples/inputs/MgB2/pseudo/Mg.pz-n-vbc.UPF b/Ladygin/examples/inputs/MgB2/pseudo/Mg.pz-n-vbc.UPF new file mode 100644 index 00000000..50554afa --- /dev/null +++ b/Ladygin/examples/inputs/MgB2/pseudo/Mg.pz-n-vbc.UPF @@ -0,0 +1,444 @@ + +Generated using ld1 code +Author: Stefano de Gironcoli Generation date: before 1998 +Info: Mg LDA 3s2 3p0 VonBarth-Car, l=2 local, core correction + 0 The Pseudo was generated with a Non-Relativistic Calculation + 0.00000000000E+00 Local Potential cutoff radius +nl pn l occ Rcut Rcut US E pseu +3S 0 0 2.00 0.00000000000 0.00000000000 0.00000000000 +3P 0 1 0.00 0.00000000000 0.00000000000 0.00000000000 + + + + + 0 Version Number + Mg Element + NC Norm - Conserving pseudopotential + T Nonlinear Core Correction + SLA PZ NOGX NOGC PZ Exchange-Correlation functional + 2.00000000000 Z valence + 0.00000000000 Total energy + 0.0000000 0.0000000 Suggested cutoff for wfc and rho + 1 Max angular momentum component + 171 Number of points in mesh + 2 2 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1.49126032066E-28 2.00640872576E-30 + diff --git a/Ladygin/examples/inputs/MgB2/pw.in b/Ladygin/examples/inputs/MgB2/pw.in new file mode 100644 index 00000000..3176f867 --- /dev/null +++ b/Ladygin/examples/inputs/MgB2/pw.in @@ -0,0 +1,32 @@ +&control + calculation = 'scf' + restart_mode = 'from_scratch' + prefix = 'mgb2' + pseudo_dir = './' + outdir = './' +/ +&system + ibrav = 4 + celldm(1) = 5.8260252227888 + celldm(3) = 1.1420694129095 + nat = 3 + ntyp = 2 + ecutwfc = 40 + smearing = 'fermi-dirac' + occupations = 'smearing' + degauss = 0.02 +/ +&electrons + diagonalization = 'david' + mixing_beta = 0.7 + conv_thr = 1.0d-12 +/ +ATOMIC_SPECIES + Mg 24.305 Mg.pz-n-vbc.UPF + B 10.811 B.pz-vbc.UPF +ATOMIC_POSITIONS crystal + Mg 0.000000000 0.000000000 0.000000000 + B 0.333333333 0.666666667 0.500000000 + B 0.666666667 0.333333333 0.500000000 +K_POINTS AUTOMATIC + 2 2 2 0 0 0 diff --git a/Ladygin/examples/inputs/Si/pseudo/Si-PBE.upf b/Ladygin/examples/inputs/Si/pseudo/Si-PBE.upf new file mode 100644 index 00000000..0bba6c59 --- /dev/null +++ b/Ladygin/examples/inputs/Si/pseudo/Si-PBE.upf @@ -0,0 +1,6765 @@ + + + +This pseudopotential file has been produced using the code +ONCVPSP (Optimized Norm-Conservinng Vanderbilt PSeudopotential) +fully-relativistic version 3.3.0 08/16/2017 by D. R. Hamann +The code is available through a link at URL www.mat-simresearch.com. +Documentation with the package provides a full discription of the +input data below. + + +While it is not required under the terms of the GNU GPL, it is +suggested that you cite D. R. Hamann, Phys. Rev. B 88, 085117 (2013) +in any publication using these pseudopotentials. + + +# ATOM AND REFERENCE CONFIGURATION +# atsym z nc nv iexc psfile +Si 14.00 3 2 4 both +# +# n l f energy (Ha) +1 0 2.00 +2 0 2.00 +2 1 6.00 +3 0 2.00 +3 1 2.00 +# +# PSEUDOPOTENTIAL AND OPTIMIZATION +# lmax +2 +# +# l, rc, ep, ncon, nbas, qcut +0 1.60000 -0.39735 4 9 6.40000 +1 1.71000 -0.14959 4 9 6.40000 +2 1.90000 0.05000 4 9 6.40000 +# +# LOCAL POTENTIAL +# lloc, lpopt, rc(5), dvloc0 +4 5 1.60000 0.00000 +# +# VANDERBILT-KLEINMAN-BYLANDER PROJECTORs +# l, nproj, debl +0 2 2.50000 +1 2 2.49961 +2 2 1.50000 +# +# MODEL CORE CHARGE +# icmod, fcfact, rcfact +3 4.00000 1.30000 +# +# LOG DERIVATIVE ANALYSIS +# epsh1, epsh2, depsh +-12.00 12.00 0.02 +# +# OUTPUT GRID +# rlmax, drl +6.00 0.01 +# +# TEST CONFIGURATIONS +# ncnf +0 +# nvcnf +# n l f + + + + + + + + +0.0000 0.0100 0.0200 0.0300 0.0400 0.0500 0.0600 0.0700 +0.0800 0.0900 0.1000 0.1100 0.1200 0.1300 0.1400 0.1500 +0.1600 0.1700 0.1800 0.1900 0.2000 0.2100 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file mode 100644 index 00000000..e77fd801 --- /dev/null +++ b/Ladygin/examples/inputs/Si/pw.in @@ -0,0 +1,41 @@ +&CONTROL + calculation = 'scf' + restart_mode = 'from_scratch' + wf_collect = .true. + tstress = .true. + tprnfor = .true. + outdir = '/pscratch/sd/v/vladygin/GitHub/rewotes/Ladygin/test_case' + prefix = 'si' + pseudo_dir = '/pscratch/sd/v/vladygin/GitHub/rewotes/Ladygin/test_case/pseudo' +/ +&SYSTEM + ecutwfc = 40 + ntyp = 1 + nat = 2 + ibrav = 0 +/ +&ELECTRONS + conv_thr = 1e-13 + mixing_beta = 0.7 + diagonalization = 'david' +/ +&IONS +/ +&CELL +/ + +ATOMIC_SPECIES +Si 28.085 Si-PBE.upf + +K_POINTS automatic +18 18 18 0 0 0 + +CELL_PARAMETERS angstrom +-2.71520825606202 0.00000000000000 2.71520825606202 +0.00000000000000 2.71520825606202 2.71520825606202 +-2.71520825606202 2.71520825606202 0.00000000000000 + +ATOMIC_POSITIONS angstrom +Si 0.0000000000 0.0000000000 0.0000000000 +Si 1.3576041280 1.3576041280 1.3576041280 + diff --git a/Ladygin/examples/run.sh b/Ladygin/examples/run.sh new file mode 100755 index 00000000..7d70008d --- /dev/null +++ b/Ladygin/examples/run.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +module load python +conda activate tdep +source /pscratch/sd/v/vladygin/doped-Si_project/MLFF_TDEP/testbench/bin/activate + +export SLURM_CPU_BIND="cores" +export OMP_PROC_BIND=spread +export OMP_PLACES=threads +export OMP_NUM_THREADS=1 +export HDF5_USE_FILE_LOCKING=FALSE + +module load PrgEnv-nvidia +module load cuda +module unload cudatoolkit/12.2 +#module load nvhpc/23.1 +module load cray-libsci/23.02.1.1 +module load cray-hdf5-parallel +module swap gpu cpu + +curr_path=$( pwd ) + +for mat in CsPbBr3; do + cp -r inputs/${mat} calcs + ConvTrack -workdir "${curr_path}/calcs/${mat}" -mode "qe" -target "total_energy" -eps 0.01 -input "pw.in" -encut 40 -calc "par" -ncores 8 -nk 8 -k_range 2 80 4 + +done From ee3638aaa989b8f9a453f3d9bdf4fa2529493d5b Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Sun, 20 Apr 2025 22:55:18 -0700 Subject: [PATCH 09/13] Changes --- Ladygin/Changes.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/Ladygin/Changes.txt b/Ladygin/Changes.txt index 4c2c51cd..0f02468d 100644 --- a/Ladygin/Changes.txt +++ b/Ladygin/Changes.txt @@ -24,3 +24,5 @@ Day 4: 04/19/25 - Added parrallelization option to the driver and job scheduler - Organized the lib in the modular fation - separated the metrix into the class +- Started computing for other systems +- Made summery notebook From 2fc8a73d6e09244d676f00404bc17cdfc3a35b6b Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Mon, 21 Apr 2025 21:57:41 -0700 Subject: [PATCH 10/13] - Added pytest - Finished calculation for 10 materials - Removed unnesessury tests inside the folder - Added error for vectors --- Ladygin/Changes.txt | 11 +- .../convergence_tracker_base.py | 41 +- .../convergence_tracker_qe.py | 27 +- .../driver/calculator/calculator_base.py | 14 +- .../driver/calculator/calculator_qe.py | 13 +- .../ConvergenceTracker/driver/driver_base.py | 27 +- .../ConvergenceTracker/driver/driver_qe.py | 31 +- Ladygin/ConvergenceTracker/search/__init__.py | 2 + .../search/kpoint_scheduler.py | 17 +- Ladygin/ConvergenceTracker/search/metrix.py | 24 +- Ladygin/ConvergenceTracker/utils/__init__.py | 3 + Ladygin/USAGE | 16 + Ladygin/examples/Convergence_summery.ipynb | 727 +- Ladygin/examples/calcs/Al/stats.dat | 15 + Ladygin/examples/calcs/CsPbBr3/stats.dat | 25 + Ladygin/examples/calcs/Cu2O/stats.dat | 8 + Ladygin/examples/calcs/Fe/stats.dat | 20 + Ladygin/examples/calcs/GaAs/stats.dat | 13 + Ladygin/examples/calcs/MgB2/stats.dat | 7 + Ladygin/examples/calcs/NaCl/stats.dat | 4 + Ladygin/examples/calcs/Ni/stats.dat | 14 + Ladygin/examples/calcs/Pb/stats.dat | 10 + Ladygin/examples/calcs/Si/stats.dat | 6 + Ladygin/examples/calcs/SiO2/stats.dat | 7 + Ladygin/examples/calcs/ZrO2/stats.dat | 12 + .../.ipynb_checkpoints/pw-checkpoint.in | 6 +- .../Cs.pbe-spn-kjpaw_psl.1.0.0-checkpoint.UPF | 39658 ++++++++++++++++ .../{Br.upf => Br.pbe-dn-kjpaw_psl.1.0.0.UPF} | 0 .../pseudo/Cs.pbe-spn-kjpaw_psl.1.0.0.UPF | 39658 ++++++++++++++++ Ladygin/examples/inputs/CsPbBr3/pseudo/Cs.upf | 39658 ---------------- .../{Pb.upf => Pb.pbe-dn-kjpaw_psl.1.0.0.UPF} | 0 Ladygin/examples/inputs/CsPbBr3/pw.in | 12 +- .../Cu2O/.ipynb_checkpoints/pw-checkpoint.in | 39 + .../Cu2O/pseudo/Cu.pbe-dn-kjpaw_psl.1.0.0.UPF | 20389 ++++++++ .../Cu2O/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF | 9766 ++++ Ladygin/examples/inputs/Cu2O/pw.in | 39 + .../GaAs/pseudo/As.pbe-n-kjpaw_psl.1.0.0.UPF | 12307 +++++ .../GaAs/pseudo/Ga.pbe-dn-kjpaw_psl.1.0.0.UPF | 20565 ++++++++ Ladygin/examples/inputs/GaAs/pw.in | 34 + .../NaCl/pseudo/Cl.pbe-n-kjpaw_psl.0.1.UPF | 10063 ++++ .../NaCl/pseudo/Na.pbe-spn-kjpaw_psl.0.2.UPF | 10041 ++++ Ladygin/examples/inputs/NaCl/pw.in | 32 + .../inputs/Ni/pseudo/ni_pbe_v1.4.uspp.F.UPF | 8115 ++++ Ladygin/examples/inputs/Ni/pw.in | 37 + .../Pb/pseudo/Pb.pbe-dn-kjpaw_psl.1.0.0.UPF | 24057 ++++++++++ Ladygin/examples/inputs/Pb/pw.in | 32 + .../inputs/SiO2/SiO2_mp-8352_primitive.cif | 32 + .../SiO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF | 9766 ++++ .../inputs/SiO2}/pseudo/Si-PBE.upf | 0 Ladygin/examples/inputs/SiO2/pw.in | 43 + .../O.pw-mt_fhi-checkpoint.UPF | 1770 + .../inputs/ZnO/pseudo/O.pw-mt_fhi.UPF | 1770 + .../inputs/ZnO/pseudo/Zn.pw-mt_fhi.UPF | 1952 + Ladygin/examples/inputs/ZnO/pw.in | 36 + .../ZrO2/.ipynb_checkpoints/pw-checkpoint.in | 40 + .../ZrO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF | 9766 ++++ .../pseudo/Zr.pbe-spn-kjpaw_psl.1.0.0.UPF | 21787 +++++++++ Ladygin/examples/inputs/ZrO2/pw.in | 36 + Ladygin/examples/run.sh | 5 +- Ladygin/test_case/pw.out | 280 - Ladygin/test_case/run.sh | 21 - Ladygin/tests/reference/pseudo/Si-PBE.upf | 6765 +++ Ladygin/{test_case => tests/reference}/pw.in | 6 +- Ladygin/tests/test_convtrack.py | 99 + 64 files changed, 249684 insertions(+), 40092 deletions(-) create mode 100644 Ladygin/ConvergenceTracker/search/__init__.py create mode 100644 Ladygin/ConvergenceTracker/utils/__init__.py create mode 100644 Ladygin/USAGE create mode 100644 Ladygin/examples/calcs/Al/stats.dat create mode 100644 Ladygin/examples/calcs/CsPbBr3/stats.dat create mode 100644 Ladygin/examples/calcs/Cu2O/stats.dat create mode 100644 Ladygin/examples/calcs/Fe/stats.dat create mode 100644 Ladygin/examples/calcs/GaAs/stats.dat create mode 100644 Ladygin/examples/calcs/MgB2/stats.dat create mode 100644 Ladygin/examples/calcs/NaCl/stats.dat create mode 100644 Ladygin/examples/calcs/Ni/stats.dat create mode 100644 Ladygin/examples/calcs/Pb/stats.dat create mode 100644 Ladygin/examples/calcs/Si/stats.dat create mode 100644 Ladygin/examples/calcs/SiO2/stats.dat create mode 100644 Ladygin/examples/calcs/ZrO2/stats.dat create mode 100644 Ladygin/examples/inputs/CsPbBr3/pseudo/.ipynb_checkpoints/Cs.pbe-spn-kjpaw_psl.1.0.0-checkpoint.UPF rename Ladygin/examples/inputs/CsPbBr3/pseudo/{Br.upf => Br.pbe-dn-kjpaw_psl.1.0.0.UPF} (100%) create mode 100644 Ladygin/examples/inputs/CsPbBr3/pseudo/Cs.pbe-spn-kjpaw_psl.1.0.0.UPF delete mode 100644 Ladygin/examples/inputs/CsPbBr3/pseudo/Cs.upf rename Ladygin/examples/inputs/CsPbBr3/pseudo/{Pb.upf => Pb.pbe-dn-kjpaw_psl.1.0.0.UPF} (100%) create mode 100644 Ladygin/examples/inputs/Cu2O/.ipynb_checkpoints/pw-checkpoint.in create mode 100644 Ladygin/examples/inputs/Cu2O/pseudo/Cu.pbe-dn-kjpaw_psl.1.0.0.UPF create mode 100644 Ladygin/examples/inputs/Cu2O/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF create mode 100644 Ladygin/examples/inputs/Cu2O/pw.in create mode 100644 Ladygin/examples/inputs/GaAs/pseudo/As.pbe-n-kjpaw_psl.1.0.0.UPF create mode 100644 Ladygin/examples/inputs/GaAs/pseudo/Ga.pbe-dn-kjpaw_psl.1.0.0.UPF create mode 100644 Ladygin/examples/inputs/GaAs/pw.in create mode 100644 Ladygin/examples/inputs/NaCl/pseudo/Cl.pbe-n-kjpaw_psl.0.1.UPF create mode 100644 Ladygin/examples/inputs/NaCl/pseudo/Na.pbe-spn-kjpaw_psl.0.2.UPF create mode 100644 Ladygin/examples/inputs/NaCl/pw.in create mode 100644 Ladygin/examples/inputs/Ni/pseudo/ni_pbe_v1.4.uspp.F.UPF create mode 100644 Ladygin/examples/inputs/Ni/pw.in create mode 100644 Ladygin/examples/inputs/Pb/pseudo/Pb.pbe-dn-kjpaw_psl.1.0.0.UPF create mode 100644 Ladygin/examples/inputs/Pb/pw.in create mode 100644 Ladygin/examples/inputs/SiO2/SiO2_mp-8352_primitive.cif create mode 100644 Ladygin/examples/inputs/SiO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF rename Ladygin/{test_case => examples/inputs/SiO2}/pseudo/Si-PBE.upf (100%) create mode 100644 Ladygin/examples/inputs/SiO2/pw.in create mode 100644 Ladygin/examples/inputs/ZnO/pseudo/.ipynb_checkpoints/O.pw-mt_fhi-checkpoint.UPF create mode 100644 Ladygin/examples/inputs/ZnO/pseudo/O.pw-mt_fhi.UPF create mode 100644 Ladygin/examples/inputs/ZnO/pseudo/Zn.pw-mt_fhi.UPF create mode 100644 Ladygin/examples/inputs/ZnO/pw.in create mode 100644 Ladygin/examples/inputs/ZrO2/.ipynb_checkpoints/pw-checkpoint.in create mode 100644 Ladygin/examples/inputs/ZrO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF create mode 100644 Ladygin/examples/inputs/ZrO2/pseudo/Zr.pbe-spn-kjpaw_psl.1.0.0.UPF create mode 100644 Ladygin/examples/inputs/ZrO2/pw.in delete mode 100644 Ladygin/test_case/pw.out delete mode 100755 Ladygin/test_case/run.sh create mode 100644 Ladygin/tests/reference/pseudo/Si-PBE.upf rename Ladygin/{test_case => tests/reference}/pw.in (89%) create mode 100644 Ladygin/tests/test_convtrack.py diff --git a/Ladygin/Changes.txt b/Ladygin/Changes.txt index 0f02468d..47aeb1d0 100644 --- a/Ladygin/Changes.txt +++ b/Ladygin/Changes.txt @@ -12,17 +12,24 @@ Day 2; 04/18/25 - Organized as a lib - Created main script -Day 3; 04/18/25 +Day 3; 04/19/25 - Finished debugging lib and binary - Added stats save - Separated job_runner into separate class - made remodelling of class inits -Day 4: 04/19/25 +Day 4: 04/20/25 - Added parrallelization option to the driver and job scheduler - Organized the lib in the modular fation - separated the metrix into the class - Started computing for other systems - Made summery notebook + +Day 5: 04/21/25 + +- Added pytest +- Finished calculation for 10 materials +- Removed unnesessury tests inside the folder +- Added error for vectors diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_base.py b/Ladygin/ConvergenceTracker/convergence_tracker_base.py index eee24b54..d5e7a064 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_base.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_base.py @@ -1,17 +1,33 @@ +from abc import ABC, abstractmethod import pickle import os + + from .driver import Driver -from .search import kpoint_scheduler +from .search import kpoint_scheduler, metrix + -class ConvergenceTracker(): + +class ConvergenceTracker(ABC): """Base class for convergence tracker Contains methods nessasure for any conv tracker""" - - def __init__(self, workdir: str, target:str, eps: float, driver: Driver) -> None: + + @abstractmethod + def __init__(self, workdir: str, + target:str, + eps: float, + driver: Driver, + k_sch: kpoint_scheduler, + metrix: metrix, + **kwargs) -> None: """workdir - directory for input files and calculations target - target property to optimize encut - kinetic energy cutoff (in eV) eps - convergence criteria (in meV) + + driver - driver for calculations + k_sch - kpoint scheduler for seach kpoint gen + metrix - metrix to compute an error """ self.workdir = workdir @@ -19,24 +35,31 @@ def __init__(self, workdir: str, target:str, eps: float, driver: Driver) -> None self.eps = eps self.kpoint_opt = None # optimal kpoint value + self.driver = driver + self.k_sch = k_sch + self.metrix = metrix + + @abstractmethod def save_stat(self, k_list: list, target_list: list, errors: list) -> None: """Save stats of kpoint convergence iterations""" - pass - + raise NotImplementedError + + @abstractmethod def _step(self, k_curr: int) -> float: """Step of the kpoint scheduler k_curr - current k point """ - pass - + raise NotImplementedError + + @abstractmethod def find_opt(self, driver: Driver, k_sch: kpoint_scheduler) -> None: """Finds optimal kpoint by running simulations and refining the parameter until convergence reached driver - drives for simulations k_sch - kpoint generator """ - pass + raise NotImplementedError def save(self, name:str) -> None: """save class as self.name.dat""" diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py index ecd6526a..e8ff5a3d 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_qe.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_qe.py @@ -16,7 +16,8 @@ class ConvergenceTrackerQE(ConvergenceTracker): def __init__(self, workdir: str = './', target:str = 'total_energy', eps:float = 1e-2, driver: DriverQE = DriverQE(), k_sch: kpoint_scheduler = kpoint_scheduler_uniform(2, 30), - metrix: metrix = mae()) -> None: + metrix: metrix = mae(), + **kwargs) -> None: """workdir - directory for input files and calculations target - target property to optimize encut - kinetic energy cutoff (in eV) @@ -26,13 +27,7 @@ def __init__(self, workdir: str = './', target:str = 'total_energy', eps:float = k_sch - kpoint scheduler for seach kpoint gen metrix - metrix to compute an error """ - self.workdir = workdir - self.target = target - self.eps = eps - - self.driver = driver - self.k_sch = k_sch - self.metrix = metrix + super().__init__(workdir, target, eps, driver, k_sch, metrix, **kwargs) def save_stat(self, k_list: list, target_list: list, errors: list) -> None: @@ -96,18 +91,4 @@ def find_opt(self) -> tuple: # Saving statistics self.save_stat(k_list[1:], target_list[2:], errors[2:]) - return k_list[1:], target_list[2:], errors[2:] - -if __name__ == """__main__""": - - driver = DriverQE(workdir = './', - input_file_name = "pw.in", - encut = 40) - - Tracker = ConvergenceTrackerQE('./', 'total_energy', - driver = driver) - - k_list, target_list, errors = Tracker.find_opt() - - - print(f"Optimal/Final value of kpoint is {k_list[-1]} with error of {errors[-1]*1e3} meV") \ No newline at end of file + return k_list[1:], target_list[2:], errors[2:] \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/driver/calculator/calculator_base.py b/Ladygin/ConvergenceTracker/driver/calculator/calculator_base.py index 61e44caf..250fcc04 100644 --- a/Ladygin/ConvergenceTracker/driver/calculator/calculator_base.py +++ b/Ladygin/ConvergenceTracker/driver/calculator/calculator_base.py @@ -1,12 +1,14 @@ -class calculator(): - """Class for point calculations""" +from abc import ABC, abstractmethod +class calculator(ABC): + """Class for point calculations""" - def __init__(self, path_to_exec:str) -> None: + @abstractmethod + def __init__(self, path_to_exec: str, **kwargs) -> None: """path_to_exec - path to executable""" - pass - + self.path_to_exec = path_to_exec + @abstractmethod def run_scf(self, input_file:str, output_file:str) -> None: """Runs scf calculation""" - pass + raise NotImplementedError diff --git a/Ladygin/ConvergenceTracker/driver/calculator/calculator_qe.py b/Ladygin/ConvergenceTracker/driver/calculator/calculator_qe.py index 622db619..9dd4d192 100644 --- a/Ladygin/ConvergenceTracker/driver/calculator/calculator_qe.py +++ b/Ladygin/ConvergenceTracker/driver/calculator/calculator_qe.py @@ -8,9 +8,9 @@ class calculator_qe(calculator): """Class for point calculations using quantum espresso""" - def __init__(self, path_to_exec:str = "/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin") -> None: + def __init__(self, path_to_exec:str = "/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin", **kwargs) -> None: """path_to_exec - path to executable""" - self.path_to_exec = path_to_exec + super().__init__(path_to_exec, **kwargs) def run_scf(self, input_file:str, output_file:str) -> None: @@ -29,12 +29,13 @@ class calculator_qe_par(calculator_qe): def __init__(self, path_to_exec:str = "/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin", ncores: int = 1, nk: int = 1, job: bool = False, - workdir: str = None) -> None: + workdir: str = None, + **kwargs) -> None: """path_to_exec - path to executable (kept for testing perposes better remove) ncores - number of cores for parallelization nk - quantum espresso parallelization setting along q """ - self.path_to_exec = path_to_exec + super().__init__(path_to_exec, **kwargs) self.ncores = ncores self.nk = nk @@ -62,10 +63,10 @@ def run_scf(self, input_file:str, output_file:str) -> None: if self.ncores > 128: raise ValueError("Number of cores is greater than number of cores per node") - with open(f"{job_file}", "a") as file: + with open(f"{job_file}", "w") as file: file.write(f"#!/bin/bash\n") file.write(f"srun -n {self.ncores} {binary} -nk {self.nk} -input {input_file} > {output_file}\n") os.remove(output_file) sp.run(f"sbatch -J qe_scf -q premium -C cpu -t 1:00:00 -N 1 -n {self.ncores} {job_file}", shell = True) - wait_qe(output_file) \ No newline at end of file + wait_qe(output_file) diff --git a/Ladygin/ConvergenceTracker/driver/driver_base.py b/Ladygin/ConvergenceTracker/driver/driver_base.py index e498cbf1..9455a671 100644 --- a/Ladygin/ConvergenceTracker/driver/driver_base.py +++ b/Ladygin/ConvergenceTracker/driver/driver_base.py @@ -1,30 +1,39 @@ +from abc import ABC, abstractmethod + + from .calculator import calculator -class Driver(): +class Driver(ABC): """Base class for running simulation""" - def __init__(self, workdir: str, calculator:calculator, input_file_name: str, encut: float) -> None: - """input_file - simulation settings input file name + + @abstractmethod + def __init__(self, workdir: str, calculator:calculator, input_file_name: str, encut: float, **kwargs) -> None: + """input_file_name - simulation settings input file name workdir - working directory calculator - job runner for point calculation encut - energy cutoff for point calc """ - pass - + self.workdir = workdir + self.calculator = calculator + self.input_file_name = input_file_name + self.encut = encut + @abstractmethod def gen_input(self, kpoint: float): """Generates the input file for the driver based on input parameters kpoint - kpoint dimension """ - pass + raise NotImplementedError + @abstractmethod def calc(self) -> None: """Runs point simulation in the current folder""" - pass - + raise NotImplementedError + @abstractmethod def extract_target(self, target:str) -> float: """Extracts the target from the simulation output""" - pass + raise NotImplementedError diff --git a/Ladygin/ConvergenceTracker/driver/driver_qe.py b/Ladygin/ConvergenceTracker/driver/driver_qe.py index 0d631ca2..b888dd4e 100644 --- a/Ladygin/ConvergenceTracker/driver/driver_qe.py +++ b/Ladygin/ConvergenceTracker/driver/driver_qe.py @@ -12,18 +12,23 @@ class DriverQE(Driver): - def __init__(self, workdir: str = './', calculator:calculator_qe = calculator_qe(), input_file_name: str = 'pw.in', encut: float = 40) -> None: - """input_file - simulation settings input file name + """Driver for point calculations using quantum espresso""" + + def __init__(self, + workdir: str = './', + calculator:calculator_qe = calculator_qe(), + input_file_name: str = 'pw.in', + encut: float = 40, + **kwargs) -> None: + """input_file_name - simulation settings input file name calculator - job runner for point calculation workdir - working directory encut - energy cutoff for point calc """ - self.workdir = workdir - self.calculator = calculator - self.input_file_name = input_file_name - self.input_file = os.path.join(self.workdir, self.input_file_name) - self.encut = encut + super().__init__(workdir, calculator, input_file_name, encut, **kwargs) + self.input_file = os.path.join(workdir, input_file_name) + def gen_input(self, kpoint: float) -> None: """Generates the input file for the driver based on input parameters @@ -74,14 +79,4 @@ def extract_target(self, target:str) -> float: out_file = os.path.join(self.workdir, f'{self.input_file_name.split(".")[0]}.out') data = read(out_file) if target == 'total_energy': - return data.get_total_energy() - - - -if __name__ == '__main__': - driver = DriverQE() - - driver.gen_input(4) - driver.calc() - total_energy = driver.extract_target('total_energy') - print("Total energy is equal to", total_energy, 'eV') \ No newline at end of file + return data.get_total_energy() \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/search/__init__.py b/Ladygin/ConvergenceTracker/search/__init__.py new file mode 100644 index 00000000..ac5f0959 --- /dev/null +++ b/Ladygin/ConvergenceTracker/search/__init__.py @@ -0,0 +1,2 @@ +from .metrix import metrix, mae +from .kpoint_scheduler import kpoint_scheduler, kpoint_scheduler_uniform \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/search/kpoint_scheduler.py b/Ladygin/ConvergenceTracker/search/kpoint_scheduler.py index d2a78119..586f88bd 100644 --- a/Ladygin/ConvergenceTracker/search/kpoint_scheduler.py +++ b/Ladygin/ConvergenceTracker/search/kpoint_scheduler.py @@ -1,14 +1,20 @@ -class kpoint_scheduler(): +from abc import ABC, abstractmethod + + +class kpoint_scheduler(ABC): """Base class for updating kpoints using convergence search""" - def __init__(self, k_start: int, k_end: int) -> None: + @abstractmethod + def __init__(self, k_start: int, k_end: int, **kwargs) -> None: """Let's do seach on an interval [k_start, k_end] k_start - kpoint to start k_end - kpoint to end """ - pass + self.k_start = k_start + self.k_end = k_end + @abstractmethod def get_next(self, errors: list) -> int: """A way to find next kpoint based on list of errors""" pass @@ -18,7 +24,7 @@ def get_next(self, errors: list) -> int: class kpoint_scheduler_uniform(kpoint_scheduler): """Base class for updating kpoints using convergence search""" - def __init__(self, k_start: int, k_end: int, k_step: int = 2) -> None: + def __init__(self, k_start: int, k_end: int, k_step: int = 2, **kwargs) -> None: """Let's do seach on an interval [k_start, k_end] k_start - kpoint to start @@ -26,8 +32,7 @@ def __init__(self, k_start: int, k_end: int, k_step: int = 2) -> None: k_step - kpoint step """ - self.k_start = k_start - self.k_end = k_end + super().__init__(k_start, k_end) self.k_step = k_step self.k_list = range(k_start, k_end, k_step) diff --git a/Ladygin/ConvergenceTracker/search/metrix.py b/Ladygin/ConvergenceTracker/search/metrix.py index a053c7bb..6f87bcf3 100644 --- a/Ladygin/ConvergenceTracker/search/metrix.py +++ b/Ladygin/ConvergenceTracker/search/metrix.py @@ -1,17 +1,29 @@ -class metrix(): - """Measures error of convergence testing""" +from abc import ABC, abstractmethod + +class metrix(ABC): + """Measures error of convergence testing""" + + @abstractmethod def __init__(self): pass - def __call__(self, value_1: float, value_2: float) -> float: + @abstractmethod + def __call__(self, value_1, value_2) -> float: """Measures error between two points""" - pass + raise NotImplementedError class mae(metrix): """Mean absolute error for convergence testing""" + def __init__(self): + super().__init__() - def __call__(self, value_1: float, value_2: float) -> float: + + def __call__(self, value_1, value_2) -> float: """Measures error between two points as mean absolute error""" - return abs(value_1 - value_2) \ No newline at end of file + try: + return sum(map(lambda x1, x2: abs(x1 - x2), value_1, value_2))/len(value_1) + except TypeError: + return abs(value_1 - value_2) + \ No newline at end of file diff --git a/Ladygin/ConvergenceTracker/utils/__init__.py b/Ladygin/ConvergenceTracker/utils/__init__.py new file mode 100644 index 00000000..11ffe4e5 --- /dev/null +++ b/Ladygin/ConvergenceTracker/utils/__init__.py @@ -0,0 +1,3 @@ +from .consts import * +from .exceptions import * +from .io import * \ No newline at end of file diff --git a/Ladygin/USAGE b/Ladygin/USAGE new file mode 100644 index 00000000..9eb74fbe --- /dev/null +++ b/Ladygin/USAGE @@ -0,0 +1,16 @@ +Software can be run either from the lib class 'ConvergenceTrackerQE' or using ConvTrack scriptt as in ./examples/run.sh +``` +ConvTrack -workdir "${curr_path}/calcs/${mat}" -eps 0.01 -encut 40 +``` + +Standard options assume "pw.in" provided withing a working directory. User suppose to arrange working "pw.in" to ensure calculation to run. In this case workdir structure is +``` +workdir/ + pw.in + pseudo/ + pseudo.UPF +``` + +For calculations I used NERSC cluster (I do not have access to terminal and or server at exabyte.io where I can add files other than python notebooks). + + diff --git a/Ladygin/examples/Convergence_summery.ipynb b/Ladygin/examples/Convergence_summery.ipynb index 70106ea0..2cc638aa 100644 --- a/Ladygin/examples/Convergence_summery.ipynb +++ b/Ladygin/examples/Convergence_summery.ipynb @@ -16,28 +16,28 @@ "id": "010d20ff-c064-4bc6-8b05-59b7f0f4ed93", "metadata": {}, "source": [ - "### 1. Al (1 atom)" + "### 1. Al (1 atom) metal" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 53, "id": "d356cc0c-d0e1-4a44-b2a6-7f5f97706610", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.0, 60.0)" + "(0.0, 100.0)" ] }, - "execution_count": 28, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -62,22 +62,23 @@ "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", "\n", - "ax1.set_xlim(0, 60)\n", + "ax1.set_xlim(0, 100)\n", "\n", "ax2 = plt.subplot(122)\n", "\n", "\n", - "ax2.plot(data[:, 0], data[:, 2]*1e3)\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", "\n", - "ax2.set_ylabel(\"Error (meV)\", fontsize = 15)\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", "\n", - "ax2.set_xlim(0, 60)" + "ax2.set_xlim(0, 100)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 54, "id": "210c3d4c-127f-4d83-9be7-9c88f44b5f89", "metadata": {}, "outputs": [ @@ -98,28 +99,28 @@ "id": "d7b38784-c1ac-4287-92af-2028fc9b9bca", "metadata": {}, "source": [ - "### 2. Si (2 atoms)" + "### 2. Si (2 atoms) semiconductor" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 55, "id": "f7bf0e0f-bbe4-4cc9-b7c0-ec0b27a07f40", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.0, 60.0)" + "(0.0, 100.0)" ] }, - "execution_count": 29, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -144,22 +145,23 @@ "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", "\n", - "ax1.set_xlim(0, 60)\n", + "ax1.set_xlim(0, 100)\n", "\n", "ax2 = plt.subplot(122)\n", "\n", "\n", - "ax2.plot(data[:, 0], data[:, 2]*1e3)\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", "\n", - "ax2.set_ylabel(\"Error (meV)\", fontsize = 15)\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", "\n", - "ax2.set_xlim(0, 60)" + "ax2.set_xlim(0, 100)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 56, "id": "92ca6d39-01ec-4f8b-8b2e-2ef63dd82e91", "metadata": {}, "outputs": [ @@ -180,28 +182,28 @@ "id": "dac36942-3049-4c52-b9b7-a6c1a0c8b2ef", "metadata": {}, "source": [ - "### 3. MgB2 (3 atoms)" + "### 3. MgB2 (3 atoms) metal" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 57, "id": "6755b7c5-1747-4b7c-bdb5-89f722208ce9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.0, 60.0)" + "(0.0, 100.0)" ] }, - "execution_count": 31, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -226,22 +228,23 @@ "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", "\n", - "ax1.set_xlim(0, 60)\n", + "ax1.set_xlim(0, 100)\n", "\n", "ax2 = plt.subplot(122)\n", "\n", "\n", - "ax2.plot(data[:, 0], data[:, 2]*1e3)\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", "\n", - "ax2.set_ylabel(\"Error (meV)\", fontsize = 15)\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", "\n", - "ax2.set_xlim(0, 60)" + "ax2.set_xlim(0, 100)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 58, "id": "a36f163b-9d55-4f1e-ba82-0400d557cac3", "metadata": {}, "outputs": [ @@ -257,13 +260,669 @@ "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" ] }, + { + "cell_type": "markdown", + "id": "c08fc68c-54a3-402b-96b3-69ce287c6401", + "metadata": {}, + "source": [ + "### 4. NaCl (2 atoms) insulator" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "93cc92e7-63cb-4d60-9372-9f5b75604dec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 100.0)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"NaCl\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 100)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3 + 1e-7)) # added small number here, since error was smaller then what saved to stat\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", + "\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "52682d3c-aa3a-45cc-9819-d566b0d869b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 14x14x14 with error of 0.0 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "markdown", + "id": "e98d694d-45be-41f0-af9e-8822f4f2f486", + "metadata": {}, + "source": [ + "### 5. Ni (1 atom) metal, magnetic" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "1427b36f-8cd6-4eb4-a2af-01d679fd308d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 100.0)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"Ni\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 100)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", + "\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "04411576-9cc8-4903-895f-18b84e0594ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 54x54x54 with error of 0.00653 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "markdown", + "id": "1cafab16-3dff-4adf-a719-2bf9d8e49e0e", + "metadata": {}, + "source": [ + "### 6. GaAs (2 atoms) semiconductor" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "95b99cb9-53a2-44bd-b92b-82777c05379e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 100.0)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"GaAs\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 100)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", + "\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "a233c981-8233-4ea5-bbb5-300de73d6cc6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 50x50x50 with error of 0.00857 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "markdown", + "id": "3f0b1a6c-dfcc-4559-bcdf-9db3ebbf4c2e", + "metadata": {}, + "source": [ + "### 7. Cu2O (6 atoms) semiconductor" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "06cc0d12-6169-403d-9d09-195a30ab7d2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 100.0)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ZVQcAAPDyJgysqmvr1DgCAKB2BEdUTblxJDgCAAB4WS1NpcaR4AgAgNoRHFE17jgCAABYfeXGkVV1AADUkOCIqrGqDgAAYPW1loMjjSMAAGpHcETVlFfVaRwBAAC8rJbm/lV1izSOAACoIcERVVNqHAmOAAAAXl55VV2nxhEAALUjOKJqyo2j7uU1ngQAAGD4a23qbxy54wgAgFoSHFE1LwRHfTWeBAAAYPhzxxEAAMOB4IiqsaoOAABg9bX+w6q6YrFY42kAAFhfCY6omlJw1CU4AgAAeFkTmvtX1XUv7/MLeAAA1IzgiKoprarrcMcRAADAy2puGJXRowpJrKsDAKB2BEdUzQur6txxBAAA8HIKhUJamvpbR4s6ums8DQAA6yvBEVVTahx1dluxAAAAsDomDNxz1K5xBABAjQiOqJoXGkdW1QEAAKyO1oHgqK1TcAQAQG0IjqgajSMAAIA109psVR0AALUlOKJq3HEEAACwZlqbBhpHVtUBAFAjgiOq5oXGkVV1AAAAq6O8qk7jCACAGhEcUTUvNI6sqgMAAFgdpVV1GkcAANSK4IiqcccRAADAmik3jjoFRwAA1IbgiKopNY663HEEAACwWiaUG0dW1QEAUBuCI6qmeaBx1N3bl+W9wiMAAICX09pUuuNI4wgAgNoQHFE1jQONo8Q9RwAAAKujZWBV3SLBEQAANSI4omrG1NelUOj/s+AIAADg5ZVW1bV3dqdYLNZ4GgAA1keCI6qmUCikeaB11NktOAIAAHg5rQONo57eYpb6OQoAgBoQHFFVTQP3HGkcAQAAvLym0aPSUN//o3pbR3eNpwEAYH0kOKKqGjWOAAAAVluhUEhrU3/rqM09RwAA1IDgiKpqEhwBAACskdI9R4IjAABqQXBEVTVbVQcAALBGWgbuOWrrtKoOAIChJziiqsqr6gRHAAAAq6W0qm6RxhEAADUgOKKqmgYaRx1W1QEAAKyW0qq69g6NIwAAhp7giKoqrarr0jgCAABYLa2lVXUaRwAA1IDgiKoqr6rTOAIAAFgtrQONI6vqAACoBcERVdXkjiMAAIA1UmoctXdaVQcAwNATHFFVpVV1GkcAAACrp7WpPzjSOAIAoBYER1SVxhEAAMCaKa2qa+vQOAIAYOgJjqiqRo0jAACANVJaVdemcQQAQA0IjqiqUuOoQ+MIAABgtUwoNY46e1IsFms8DQAA6xvBEVVVuuOoS+MIAABgtZQaR719xTy/bHmNpwEAYH0jOKKqGt1xBAAAsEYaR49K4+j+H9etqwMAYKgJjqiq8qo6jSMAAIDV1to0sK5OcAQAwBATHFFVzQ31SZIujSMAAIDVVlpXt6iju8aTAACwvhEcUVVNDf3fYlbVAQAArL5ScNTWqXEEAMDQEhxRVY1W1QEAAKyx0qq6do0jAACGmOCIqiqvqhMcAQAArLYJY0ur6jSOAAAYWoIjqqppoHFkVR0AAMDqaxloHLUJjgAAGGLrTHB0wQUXZMstt0xjY2NmzJiR3/3udy95/qc//Wm23377NDY2Zqeddso111yzwuPFYjGnnXZaJk+enKampsyaNSt/+ctfVjjz3HPP5dBDD8348ePT2tqaI488Ms8//3z58RtuuCHvfve7M3ny5IwdOzbTp0/PJZdcUrlPegQoBUfL+4rp6e2r8TQAAAAjQ/mOI6vqAAAYYutEcPTjH/84J5xwQk4//fTcfffdmTZtWvbbb7889dRTKz1/66235pBDDsmRRx6Ze+65J7Nnz87s2bPzwAMPlM985StfybnnnpsLL7wwt99+e8aOHZv99tsvXV1d5TOHHnpo/vCHP+S6667LVVddlZtuuilHH330Cq+z88475/LLL8/999+fI444Iocffniuuuqq6n0xhpmmhlHlP7vnCAAAYPVMKAVHnRpHAAAMrUKxWCzWeoi1NWPGjLz2ta/N+eefnyTp6+vL1KlT84lPfCInnXTSi84fdNBBWbp06QoBzute97pMnz49F154YYrFYqZMmZJPfepT+fSnP50kaW9vz8SJE/Pd7343Bx98cB588MHssMMOueOOO7L77rsnSebMmZO3v/3tefzxxzNlypSVznrAAQdk4sSJufjii1frc1u8eHFaWlrS3t6e8ePHr9HXZTgoFot55b/+Ir19xdz+ubdk4vjGWo8EAMAwN9L/DczQWxe/Z+Y8sCAf++Fd2WXz1lzxz6+v9TgAAAwj1f7374hvHHV3d+euu+7KrFmzyu+rq6vLrFmzctttt630ObfddtsK55Nkv/32K59/+OGHs2DBghXOtLS0ZMaMGeUzt912W1pbW8uhUZLMmjUrdXV1uf3221c5b3t7ezbYYINVPr5s2bIsXrx4hbeRrFAovHDPkcYRAADAaik1jtrdcQQAwBAb8cHRM888k97e3kycOHGF90+cODELFixY6XMWLFjwkudL/325M5tssskKj9fX12eDDTZY5ev+5Cc/yR133JEjjjhilZ/PmWeemZaWlvLb1KlTV3l2pGgcCI6sqgMAAFg9rc0NSayqAwBg6I344Gik+PWvf50jjjgiF110UV7zmtes8tzJJ5+c9vb28tu8efOGcMrqaB6456izR3AEAACwOlpLdxx1dKevb8RvmAcAYAQZ8cHRRhttlFGjRmXhwoUrvH/hwoWZNGnSSp8zadKklzxf+u/LnXnqqadWeHz58uV57rnnXvS6N954Y975znfm7LPPzuGHH/6Sn8+YMWMyfvz4Fd5GutKqui7BEQAAwGppaeoPjvqKyZJly2s8DQAA65MRHxw1NDRkt912y9y5c8vv6+vry9y5czNz5syVPmfmzJkrnE+S6667rnx+q622yqRJk1Y4s3jx4tx+++3lMzNnzkxbW1vuuuuu8pnrr78+fX19mTFjRvl9N9xwQw444IB8+ctfztFHH732n/AI1NhgVR0AAMCaaBw9qvxLeG0d3TWeBgCA9Ul9rQeohBNOOCEf+tCHsvvuu2ePPfbIOeeck6VLl5bvEjr88MOz6aab5swzz0ySHHfccdl7771z1lln5YADDshll12WO++8M9/61reSJIVCIccff3zOOOOMbLvtttlqq61y6qmnZsqUKZk9e3aS5NWvfnX233//HHXUUbnwwgvT09OTY489NgcffHCmTJmSpH893Tve8Y4cd9xxee9731u++6ihoSEbbLDBEH+Vaqd5tFV1AAAAa2pC8+h0tvemraMnW2xY62kAAFhfrBPB0UEHHZSnn346p512WhYsWJDp06dnzpw5mThxYpLkscceS13dC+WqPffcM5deemlOOeWUfO5zn8u2226bK6+8MjvuuGP5zIknnpilS5fm6KOPTltbW97whjdkzpw5aWxsLJ+55JJLcuyxx+Ytb3lL6urq8t73vjfnnntu+fHvfe976ejoyJlnnlkOrZJk7733zg033FDFr8jw0jTQOOrSOAIAAFhtLc0Nmd/elbbOnlqPAgDAeqRQLBbdsjmMLV68OC0tLWlvbx+x9x0dc8ndufr3T+bf3rlDPvz6rWo9DgAAw9y68G9ghta6+j3zgYt+m1v/9my+dvD0vHv6prUeBwCAYaLa//4d8XccMfyVGkedPX01ngQAAGDkaG0enSRp69A4AgBg6AiOqLomdxwBAACssZamhiTJoo7uGk8CAMD6RHBE1ZXvOBIcAQAArLYJGkcAANSA4IiqKzWOOrqX13gSAACAkeOFVXUaRwAADB3BEVVXvuOo2x1HAAAAq6u1uX9VXVunxhEAAENHcETVlRpHVtUBAACsvtYmq+oAABh6giOqzqo6AACANVduHFlVBwDAEBIcUXXlVXUaRwAAAKttQumOI6vqAAAYQoIjqq7UOOrscccRAADA6moZCI7aO3vS21es8TQAAKwvBEdUXblxZFUdAADAamtt6l9VVywmS7q0jgAAGBqCI6rOqjoAAIA111Bfl7EDP0+1dQiOAAAYGoIjqq68qq7bqjoAAIA10drc3zpa1NFd40kAAFhfCI6ouheCI6vqAAAA1kTrwD1HbZ0aRwAADA3BEVXX/A+r6opFF7oCAACsrnJwpHEEAMAQERxRdY0DwVFfMenuta4OAABgdZVW1bnjCACAoSI4oupKq+qSpMs9RwAAAKuttanUOBIcAQAwNARHVN3oUXUZPaqQJOnocc8RAADA6ppQbhxZVQcAwNAQHDEkGgdaR53dvTWeBAAAYOQo33HUqXEEAMDQEBwxJErr6jp7BEcAAACrq2VgVd0iq+oAABgigiOGRFODxhEAAMCaKq2qa7eqDgCAISI4YkhoHAEAAKy50qo6jSMAAIaK4IghoXEEAACw5loHGkdtGkcAAAwRwRFDQuMIAABgzZUaR4u7lqe3r1jjaQAAWB8IjhgSzRpHAAAAa6ylaXT5z+2d1tUBAFB9giOGRKPGEQAAwBobPaou48bUJ7GuDgCAoSE4YkhYVQcAADA4LQPr6hZ1aBwBAFB9giOGhFV1AAAAgzOhuSFJ0t6pcQQAQPUJjhgSjYIjAACAQWkdaBy1aRwBADAEBEcMCavqAAAABqd1oHFkVR0AAENBcMSQKAdHGkcAAABrpLWpv3HU3mFVHQAA1Sc4YkiU7zjSOAIAAFgjpVV1GkcAAAwFwRFDotGqOgAAgEEprapr6xQcAQBQfYIjhkTTQOOow6o6AACANVJaVddmVR0AAENAcMSQKK2q69I4AgAAWCMTxpaCI40jAACqT3DEkCivqtM4AgAAWCMtTaVVdRpHAABUn+CIIdE02qo6AACAwWhtHmgcLdU4AgCg+gRHDInmhvokVtUBAACsqQnN/Y2jJcuWp6e3r8bTAACwrhMcMSRKjaNOwREAAMAaGd9YX/5ze6fWEQAA1SU4Ykg0NvR/q3X29KZYLNZ4GgAAgJGjflRdOTxq6xAcAQBQXYIjhkRpVV2xmCxbbrUCAADAmmgdWFfX3tld40kAAFjXCY4YEo31L3yrdXZbVwcAALAmJjSPTpIsWqpxBABAdQmOGBL1o+rSMOqFdXUAAACsvpaBxlGbO44AAKgywRFDpnF0/7dbh8YRAADAGmlt6m8ctXVYVQcAQHUJjhgypXuOujSOAAAA1khpVV1bh8YRAADVJThiyDQ1jEpiVR0AAMCaKq2qW6RxBABAlQmOGDKNo/uDI6vqAAAA1ky5ceSOIwAAqkxwxJBpLjWOBEcAAMA/uOmmm/LOd74zU6ZMSaFQyJVXXlnrkYad1oHgqN2qOgAAqkxwxJBpGmgcueMIAAD4R0uXLs20adNywQUX1HqUYau1yao6AACGRn2tB2D9YVUdAACwMm9729vytre9rdZjDGulxlGbxhEAAFUmOGLIlFfVaRwBAABrYdmyZVm2bFn574sXL67hNEOjtbm/cdSmcQQAQJVZVceQsaoOAACohDPPPDMtLS3lt6lTp9Z6pKqbMNA4Wtrdm+7lfTWeBgCAdZngiCHTVGocWVUHAACshZNPPjnt7e3lt3nz5tV6pKob1zg6hUL/n9s7rasDAKB6rKpjyJSCI3ccAQAAa2PMmDEZM2ZMrccYUqPqCmlpGp22jp60dXRn43Hr1+cPAMDQ0ThiyJRW1bnjCAAAYM21NvWvq2vTOAIAoIo0jhgy7jgCAABW5vnnn89f//rX8t8ffvjh3Hvvvdlggw2y+eab13Cy4aWluSF5tiOLlnbXehQAANZhgiOGTGN5Vd3yGk8CAAAMJ3feeWfe9KY3lf9+wgknJEk+9KEP5bvf/W6Nphp+JjRrHAEAUH2CI4ZMc3lVXV+NJwEAAIaTffbZJ8VisdZjDHulVXXtHYIjAACqxx1HDJmmgcZRV7dVdQAAAGuqtbkhSbKow6o6AACqR3DEkCndcdTRY1UdAADAmmq1qg4AgCEgOGLIlBpHnRpHAAAAa6y0qq5N4wgAgCoSHDFkSo2jLnccAQAArLEJY/tX1bW54wgAgCoSHDFkSo2jjm6r6gAAANZUy0DjaJHgCACAKhIcMWRKjaPOHqvqAAAA1tSE5v7GUbtVdQAAVJHgiCFTahx19fSlr69Y42kAAABGltbmgTuOOjWOAACoHsERQ6bUOEqSZcvdcwQAALAmWpv6G0cd3b1ZttwmBwAAqkNwxJD5x+DIPUcAAABrZlxjfeoK/X9ud88RAABVIjhiyNTVFTKmvv9bzj1HAAAAa6aurpCWpv51dYsERwAAVIngiCH1wj1HgiMAAIA1NaG5f11dW0d3jScBAGBdJThiSJXW1XV0C44AAADWVEtzf+OorVPjCACA6hAcMaRKjaNOwREAAMAa0zgCAKDaBEcMqVLjyB1HAAAAa6514I6jNnccAQBQJYIjhlQ5ONI4AgAAWGOlVXWLBEcAAFSJ4IghVV5Vp3EEAACwxkqr6to7raoDAKA6BEcMKavqAAAABq+11DhaqnEEAEB1CI4YUuXGkVV1AAAAa6x1oHHUpnEEAECVCI4YUs2CIwAAgEFrbepvHLW54wgAgCoRHDGkGq2qAwAAGLTSqjrBEQAA1SI4Yki54wgAAGDwJlhVBwBAlQmOGFLl4MiqOgAAgDXWMtA46urpS5dfyAMAoAoERwyppgaNIwAAgMEaN6Y+o+oKSayrAwCgOgRHDKlycKRxBAAAsMYKhUJamwbuObKuDgCAKhAcMaTccQQAALB2WgfW1S1aqnEEAEDlCY4YUs0aRwAAAGultbkhSdKucQQAQBUIjhhSjRpHAAAAa6W0qm6RO44AAKgCwRFDqryqTuMIAABgUEqNozbBEQAAVSA4Ykg1N9Qn0TgCAAAYrNIdR20dVtUBAFB5giOGVFND/7ec4AgAAGBwJpSDI40jAAAqT3DEkGq0qg4AAGCttJRW1XVqHAEAUHmCI4ZUaVXdsuV96e0r1ngaAACAkae1qb9xtEjjCACAKhAcMaSaBhpHSdJlXR0AAMAamzDQOGoXHAEAUAWCI4bUmPoXvuXccwQAALDmWptLjSOr6gAAqDzBEUOqrq6QxtH933buOQIAAFhzpeCorbMnxaIV4AAAVJbgiCFXuudI4wgAAGDNtQ6squte3peunr4aTwMAwLpGcMSQK91zpHEEAACw5sY2jEp9XSGJdXUAAFSe4IghV1pV1yE4AgAAWGOFQqHcOmrr6KnxNAAArGsERwy50qq6LqvqAAAABqV8z5HGEQAAFSY4YsiVV9UJjgAAAAZlQik46tQ4AgCgstaZ4OiCCy7IlltumcbGxsyYMSO/+93vXvL8T3/602y//fZpbGzMTjvtlGuuuWaFx4vFYk477bRMnjw5TU1NmTVrVv7yl7+scOa5557LoYcemvHjx6e1tTVHHnlknn/++fLjXV1d+fCHP5yddtop9fX1mT17dsU+35GssaE/OLKqDgAAYHBamqyqAwCgOtaJ4OjHP/5xTjjhhJx++um5++67M23atOy333556qmnVnr+1ltvzSGHHJIjjzwy99xzT2bPnp3Zs2fngQceKJ/5yle+knPPPTcXXnhhbr/99owdOzb77bdfurq6ymcOPfTQ/OEPf8h1112Xq666KjfddFOOPvro8uO9vb1pamrKv/zLv2TWrFnV+wKMMM0aRwAAAGul1DhaZFUdAAAVtk4ER1/96ldz1FFH5YgjjsgOO+yQCy+8MM3Nzbn44otXev5rX/ta9t9//3zmM5/Jq1/96nzhC1/IrrvumvPPPz9Jf9vonHPOySmnnJJ3v/vd2XnnnfP9738/8+fPz5VXXpkkefDBBzNnzpx8+9vfzowZM/KGN7wh5513Xi677LLMnz8/STJ27Nh84xvfyFFHHZVJkyYNyddiJGgaaBx1aRwBAAAMSumOo3ar6gAAqLARHxx1d3fnrrvuWqHRU1dXl1mzZuW2225b6XNuu+22FzWA9ttvv/L5hx9+OAsWLFjhTEtLS2bMmFE+c9ttt6W1tTW77757+cysWbNSV1eX22+/fdCfz7Jly7J48eIV3tY1jRpHAAAAa6W1uX9V3aKlGkcAAFTWiA+OnnnmmfT29mbixIkrvH/ixIlZsGDBSp+zYMGClzxf+u/Lndlkk01WeLy+vj4bbLDBKl93dZx55plpaWkpv02dOnXQH2u4anbHEQAAwFopNY7aNI4AAKiwER8crWtOPvnktLe3l9/mzZtX65EqrmmgcdSlcQQAADAorU39jaM2dxwBAFBhIz442mijjTJq1KgsXLhwhfcvXLhwlfcKTZo06SXPl/77cmeeeuqpFR5fvnx5nnvuubW6z2jMmDEZP378Cm/rmtIdR50aRwAAAIMyodQ46tA4AgCgskZ8cNTQ0JDddtstc+fOLb+vr68vc+fOzcyZM1f6nJkzZ65wPkmuu+668vmtttoqkyZNWuHM4sWLc/vtt5fPzJw5M21tbbnrrrvKZ66//vr09fVlxowZFfv81kWlO446NI4AAAAGpcWqOgAAqqS+1gNUwgknnJAPfehD2X333bPHHnvknHPOydKlS3PEEUckSQ4//PBsuummOfPMM5Mkxx13XPbee++cddZZOeCAA3LZZZflzjvvzLe+9a0kSaFQyPHHH58zzjgj2267bbbaaquceuqpmTJlSmbPnp0kefWrX539998/Rx11VC688ML09PTk2GOPzcEHH5wpU6aUZ/vjH/+Y7u7uPPfcc1myZEnuvffeJMn06dOH7Osz3DRrHAEAAKyV1uYXVtUVi8UUCoUaTwQAwLpinQiODjrooDz99NM57bTTsmDBgkyfPj1z5szJxIkTkySPPfZY6upeKFftueeeufTSS3PKKafkc5/7XLbddttceeWV2XHHHctnTjzxxCxdujRHH3102tra8oY3vCFz5sxJY2Nj+cwll1ySY489Nm95y1tSV1eX9773vTn33HNXmO3tb397Hn300fLfd9lllyRJsVisytdiJHDHEQAAwNoprarr6S2mo7s3Y8esEz/eAwAwDBSK63OCMQIsXrw4LS0taW9vX2fuO5rzwIJ87Id3ZdfNW/M///z6Wo8DAMAwsy7+G5jqWh+/Z4rFYrY7ZU66e/ty82fflM0mNNd6JAAAhki1//074u84YuQpr6rr6avxJAAAACNToVBIa+meow73HAEAUDmCI4ZcU4NVdQAAAGurFBy1dwqOAACoHMERQ650x1FH9/IaTwIAADBytTY3JEkWdXTXeBIAANYlgiOGXKlx1NmtcQQAADBYrU1W1QEAUHmCI4ZcqXHU5Y4jAACAQXvhjiONIwAAKkdwxJArBUfdvX1Z3is8AgAAGIwJA6vqNI4AAKik+loPwPqntKouSTp7ejNulPwSAACGUnd3dx588ME8/fTTaWtrS2trazbeeOO8+tWvTkNDQ63HYzW1DDSOFgmOAACoIMERQ25MfV0KhaRYHAiOGkfXeiQAAFjnPf300/nud7+bq6++Or/73e+ybNmyF50ZM2ZM9thjj7zjHe/Ihz70oWy88cY1mJTVVWoctXdaVQcAQOUIjhhyhUIhTaNHpaO7N13dVtUBAEA1/fWvf82pp56aK664It3d/QHDRhttlN122y0bbLBBxo8fn/b29ixatCh/+tOfctNNN+Wmm27KKaeckve85z3593//97zyla+s8WfByrQ2le440jgCAKByBEfURCk46uhZXutRAABgnXXsscfmoosuSm9vb970pjflAx/4QPbZZ59stdVWq3zO3//+9/z617/OpZdemp/85Ce5/PLLc/TRR+e8884bwslZHS+sqtM4AgCgctY6OGpvb3/RXuyWlpZKzMY6rKlhVLI06ezurfUoAACwzrr44ovz8Y9/PCeeeGKmTJmyWs/Zeuuts/XWW+fII4/ME088ka985Sv59re/LTgahl5YVadxBABA5axxcLR8+fJcccUVufrqq3PjjTfmsccee9GZzTffPHvvvXfe8Y53ZPbs2amvV2xiRU2jRyXpv+MIAACojr///e+ZNGnSoJ+/6aab5mtf+1pOPvnkCk5FpbQ2v7CqrlgsplAo1HgiAADWBaud6LS1teXLX/5yLr744jzzzDMpFoupq6vLlClTXrQX+7HHHsv3v//9/OAHP8hGG22UI488MieeeGJaW1ur+KkwkjQ1DARHGkcAAFA1axMaVePjUFmlxtHyvmKeX7Y84xpH13giAADWBXWrc+i//uu/ss022+TLX/5yWlpacsopp2Tu3LlZtGhR5s2bl/vuuy+/+c1vcv/992fevHlpa2vLr371q3zuc5/L+PHj86UvfSnbbLNNzjrrrGp/PowQGkcAADD0TjjhhHzhC1+o9RhUSOPoURlT3/9jfVuHdXUAAFTGagVHJ554Yvbaa6/cdttt+fOf/5zPf/7zedOb3pRx48at9Py4cePy5je/OV/4whfyl7/8Jbfcckve8IY35MQTT6zo8IxcGkcAADD0zj///Nx///21HoMK+sd1dQAAUAmrtaru7rvvzvTp0wf9IjNnzszPfvaz3HvvvYP+GKxbNI4AAGDobbbZZunr66v1GFTQhOaGLFy8LG2d3bUeBQCAdcRqNY7WJjSqxsdh5NM4AgCAoTd79uzceOONWbJkSa1HoUJamvobR4s0jgAAqJDVCo4+85nP5Pe//321Z2E9onEEAABD7/Of/3w233zzvP3tb88999xT63GogAnNDUmS9g6NIwAAKmO1VtWdddZZ+epXv5odd9wxhx12WD7wgQ9kypQp1Z6NdZjgCAAAht673/3ujBkzJrfcckt23333TJ48OZtvvnkaGxtfdLZQKGTu3Lk1mJI1UbrjSOMIAIBKWa3g6GMf+1h++tOf5ve//30++9nP5uSTT86b3vSmHHbYYXnPe96TsWPHVntO1jFW1QEAwNC74YYbyn8uFouZP39+5s+fv9KzhUJhiKZibbQONI7aBEcAAFTIagVHX//613PuuefmF7/4RX7wgx/kqquuyq9+9avMnTs3H//4xzN79ux88IMfzL777pu6utXafsd6TnAEAABD7+GHH671CFRYqXHU1mlVHQAAlbFawVGS1NfX553vfGfe+c53ZsmSJfn//r//Lz/84Q9z44035tJLL82PfvSjbLLJJjnkkEPywQ9+MLvuums152aEs6oOAACG3hZbbFHrEaiw1qaB4EjjCACAChlUPWjcuHE54ogjMnfu3Dz22GP5yle+kp122ikLFy7MOeeck9e+9rV5zWteky9/+cuZN29epWdmHVAOjjSOAAAABu2FVXUaRwAAVMZqN45WZcqUKfn0pz+dT3/60/njH/+Y73//+7nsssvy4IMP5nOf+1xOOeWU9PT4zSdWVF5Vp3EEAAA1cdttt+U3v/lNnnjiiSTJpptumr322iszZ86s8WSsifKqOo0jAAAqZK2Do3+0ww475MQTT8zUqVPz+c9/Ps8880z6+voq+RKsI6yqAwCA2vjzn/+cww47LHfeeWeSpFgsJkkKhUKSZPfdd88Pf/jDbLvttjWbkdU3odQ46hQcAQBQGRUJjpYtW5af//zn+eEPf5hf/vKX6enpSbFYzAYbbJCDDjqoEi/BOqbcOLKqDgAAhsyTTz6ZvffeOwsXLsyUKVPyvve9L1tuuWUKhUIeeeSR/PSnP80dd9yRffbZJ3feeWcmT55c65F5GS80jrrT11dMXV2hxhMBADDSrVVwdP311+eHP/xh/ud//idLlixJsVjMmDFjcuCBB+awww7L29/+9owePbpSs7IOabaqDgAAhtwZZ5yRhQsX5pOf/GTOPPPMNDQ0rPD4l7/85Zx88sn56le/mi9+8Ys577zzajQpq6ulqf9n7r5ismTZ8vLfAQBgsNY4OLrvvvtyySWX5Ec/+lHmz5+fYrGYQqGQ17/+9TnssMPy/ve/Py0tLdWYlXVI42iNIwAAGGrXXHNNtttuu5x11lkrfXz06NH5z//8z1x99dW56qqrBEcjQOPoUWkaPSqdPb1p7+gRHAEAsNZWKzh6/PHHc8kll+SSSy7JH/7whyT9e7Bf9apX5bDDDsuhhx6aLbfcsppzso5xxxEAAAy9J598Mu9973tf8kyhUMiuu+6ayy+/fIimYm21No9OZ3tvFnV0Z/MNm2s9DgAAI9xqBUdbbrllisViisViNt544xx00EE57LDD8trXvrba87GOam7o/9bTOAIAgKEzfvz4zJs372XPzZs3L+PHjx+CiaiE1uaGPNnelbbOnlqPAgDAOqBudQ6NHj0673vf+/K///u/mT9/fs4991yhEWul1Dha3ldMT29fjacBAID1w8yZM3PLLbfk6quvXuWZa665Jrfcckv23HPPIZyMtdE6sJ6uraO7xpMAALAuWK3G0VNPPZVx48ZVexbWI40NL2SWnT29GT1qtTJMAABgLZx00km55pprcuCBB+aggw7KBz7wgfLa8UcffTQ/+tGPctlll6Wuri4nnXRSbYdltU0YWwqONI4AAFh7qxUcvVRodN999+V3v/tdnnnmmbzmNa/Ju971riTJsmXLsmzZMusNWKmGUXWpKyR9xf51deMbXeAKAADVNnPmzHznO9/JRz/60VxyySW59NJLV3i8WCymqakp3/zmN/O6172uRlOyplqaGpIIjgAAqIzVCo5W5qGHHsoRRxyR22+/vfy+D33oQ+Xg6NJLL80//dM/5eqrr87++++/9pOyTikUCmluqM/zy5a75wgAAIbQBz/4weyzzz656KKLcvPNN2f+/PlJkilTpmSvvfbKkUcemalTp9Z4StZEa3P/L+ItsqoOAIAKGFRwNG/evLzxjW/M008/nXe9613Za6+98pnPfGaFM+9///vzz//8z7n88ssFR6xU4+hR/cFRj+AIAACGwgknnJAJEybk1FNPzec///laj0OFTBgIjto7NY4AAFh7g7pY5t///d/zzDPP5Nvf/nauvPLKfOpTn3rRmbFjx2b69OkrNJLgHzUN3HPUoXEEAABD4vzzz8/9999f6zGosNaBVXUaRwAAVMKggqM5c+Zk5513zkc+8pGXPLflllvmiSeeGNRgrPuaR/cX3ro0jgAAYEhsttlm6evrq/UYVFhpVZ07jgAAqIRBBUdPPfVUtttuu5c919PTk46OjsG8BOuBxoZRSeKOIwAAGCKzZ8/OjTfemCVLltR6FCqotbm/cWRVHQAAlTCo4GjDDTfMY4899rLn/vznP2fy5MmDeQnWA02jB1bVaRwBAMCQ+PznP5/NN988b3/723PPPffUehwqpNQ4sqoOAIBKqB/Mk17/+tfnyiuvzL333pvp06ev9MyNN96YBx54IB/+8IfXYjzWZc0NA6vqNI4AAGBIvPvd786YMWNyyy23ZPfdd8/kyZOz+eabp7Gx8UVnC4VC5s6dW4MpWVOl4Ki9syd9fcXU1RVqPBEAACPZoIKjT3/607niiivy7ne/OxdeeGH23XffFR6//vrr8+EPfzj19fU5/vjjKzEn66Cm0QOr6jSOAABgSNxwww3lPxeLxcyfPz/z589f6dlCQfgwUrQ29a+qKxaTxV095dV1AAAwGIMKjmbMmJFzzz03xx13XN7xjnekubk5hUIhl19+ea644oosXrw4hUIhX//617PzzjtXembWEY2CIwAAGFIPP/xwrUegChrq6zK2YVSWdvemrUNwBADA2hlUcJQk//zP/5xdd901X/rSl3L99denWCxmyZIlaWxszH777Zd//dd/zetf//pKzso6prmhPzjqsKoOAACGxBZbbFHrEaiS1uaGLO3uTFtnT61HAQBghBt0cJQkr3vd63LllVemWCzmmWeeSV9fXzbaaKOMGjWqUvOxDmsaCI66NI4AAGBInHDCCZkwYUJOPfXUWo9ChbU0jc4TbZ1Z1NFd61EAABjh6irxQQqFQjbeeONMnDhRaMRqK6+q0zgCAIAhcf755+f++++v9RhUwYSxo5Mk7R0aRwAArJ2KBEcwGE2jraoDAIChtNlmm6Wvr6/WY1AFrU399xppHAEAsLZWKzh63etel1/+8pdr9ULXXHNNZsyYsVYfg3VLs1V1AAAwpGbPnp0bb7wxS5YsqfUoVFhrc3/jqE3jCACAtbRawdGiRYvy9re/PbvsskvOOeecPPnkk6v1wefPn5+zzjor06dPzzve8Y4sXrx4rYZl3VJqHHUKjgAAYEh8/vOfz+abb563v/3tueeee2o9DhX0QnCkcQQAwNqpX51Df/zjH3PhhRfmC1/4Qk444YR8+tOfzitf+cq89rWvzXbbbZcJEyZk3LhxWbJkSZ577rk89NBDueOOO/LXv/41xWIxEydOzAUXXJCjjz662p8PI0hjQ2lV3fIaTwIAAOuHd7/73RkzZkxuueWW7L777pk8eXI233zzNDY2vuhsoVDI3LlzazAlgzGhuX9VXVunxhEAAGtntYKjUaNG5ZhjjslRRx2Vn/70p/n2t7+dm2++OX/+85+T9P9AUVIsFsvP2XvvvXPUUUflve99bxoaGqowPiNZc7lxZMc6AAAMhRtuuKH852KxmPnz52f+/PkrPfuPP+cx/LU0WVUHAEBlrFZwVNLQ0JBDDz00hx56aJYsWZJbb701999/f5566qm0t7enpaUlm2yySaZNm5Y999wzr3jFK6o1N+uAptIdR91W1QEAwFB4+OGHaz0CVdJaahxZVQcAwFpao+DoH40bNy777bdf9ttvv0rOw3qkcaBx1NFjVR0AAAyFLbbYotYjUCUTSnccWVUHAMBaqqv1AKy/mgcaR53dVtUBAACsjdaB4GjRUo0jAADWjuCImmkaaBx19VhVBwAA1fCRj3wkF1988Uof+/nPf5577713pY+dfvrp2W233ao4GZVWWlW3uGt5evuKNZ4GAICRTHBEzZTuOOrs6U2x6AcbAACotO9+97u5+eabV/rY7Nmzc+655670sccee2yVoRLDU0vT6PKfF1tXBwDAWhAcUTOl4Ki3r5juXuvqAAAABmv0qLq8Ykz/NcaLOqyrAwBg8ARH1ExpVV2SdLnnCAAAYK2U7jlq0zgCAGAtCI6omdGj6lJfV0jSv64OAACAwSsHRxpHAACsBcERNVVqHXV0L6/xJAAAACPbhOaGJElbh8YRAACDN6jg6JFHHqnwGKyvSvccaRwBAACsnZam/sbRIsERAABrYVDB0Stf+crsv//+ufzyy7N8uaYIg1cKjroERwAAAGul1Dhqt6oOAIC1MKjgaJtttsm1116b97///dlss81y0kkn5S9/+UulZ2M98MKqOsERAABUw/e+972MGjXqRW+FQmGVj33/+9+v9dgMQvmOo06NIwAABm9QwdFDDz2UX//61znkkEOyePHifOUrX8n222+fN7/5zbnsssvS3e23m1g95VV1giMAAKiKYrE4qDdGHqvqAACohEEFR0my995754c//GHmz5+fr33ta9lxxx1zww035NBDD82UKVNywgkn5MEHH6zkrKyDSo0jdxwBAEDl9fX1Dfqtt9e/0Uea0qq6NqvqAABYC4MOjkpaW1vziU98Ivfdd19++9vf5iMf+Ui6u7vLYdJee+2VH/zgB1m2bFkl5mUdUw6ONI4AAADWSnlVncYRAABrYa2Do3+0xx575NOf/nQOPvjg8nqDW265JR/+8Iez+eab5/zzz6/ky7EOKK+q0zgCAABYK62lxlGnxhEAAINXkeCoq6srP/jBD/LGN74xO+ywQ7797W9n0qRJ+dznPpdf/epX+ad/+qc8//zzOe644/KFL3yhEi/JOsKqOgAAqJ4//OEPw+rjUF0aRwAAVMJaBUf3339/PvGJT2TKlCn58Ic/nFtuuSVvetOb8pOf/CSPPfZYzjjjjLz5zW/ON7/5zfzxj3/MJptskm9961uVmp11QKlx1GVVHQAAVNzOO++cQw45JPfff/+gnn/PPffk/e9/f6ZNm1bhyaiG1qb+4GhJ1/Is7+2r8TQAAIxU9YN50re//e1cdNFFufPOO1MsFrPhhhvmk5/8ZD760Y9m2223Xelztthii7z1rW/NJZdcslYDs24pNY46BEcAAFBxp59+ev7rv/4rP/nJT7LTTjvlkEMOyd57751ddtklY8aMedH5rq6u3HPPPbnhhhty6aWX5o9//GPGjh2b008/vQbTs6ZaBoKjJGnv7MmGr3jx/40BAODlDCo4Ovroo5Mke+65Zz72sY/lfe9730p/6Pi/dt5558ybN28wL8k6yh1HAABQPaeddlo+9rGP5T/+4z/y/e9/PyeffHIKhULq6+szderUTJgwIePGjcuSJUvy3HPPZd68eent7U2xWExLS0uOO+64nHzyydl4441r/amwGupH1WVcY32WdC3Pog7BEQAAgzOo4OiYY47JRz/60ey4445r9LxPf/rT+fSnPz2Yl2Qd5Y4jAACork022SRf+9rX8qUvfSk/+clPctVVV+Xmm2/O3//+9xednTRpUvbaa68ccMABef/735/GxsYaTMzamNDckCVdy9Pe2V3rUQAAGKEGFRydd955lZ6D9VS5cWRVHQAAVFVTU1M+9KEP5UMf+lCS5Omnn85TTz2V9vb2tLS0ZJNNNtEsWge0No/OY88lbR09tR4FAIARalDBEVSKxhEAANTGxhtvLChaB5XuOVokOAIAYJAGFRy9+c1vXq1zDQ0N2XDDDTN9+vQcfPDBmTp16mBejnWYxhEAAEDlTGhuSJK0dVhVBwDA4AwqOLrhhhuSJIVCIcVicaVn/vGxH/3oRznllFPy5S9/Occff/ygBmXdpHEEAABQOa3N/Y0jq+oAABisusE86eGHH85xxx2X+vr6HHroofn5z3+ee++9N/fee2/+93//Nx/84AdTX1+fT3ziE7n55pvzxS9+MY2NjfnUpz6Va6+9ttKfAyOYxhEAAEDltJYaR50aRwAADM6gGke//e1vc9555+UXv/hF3vrWt67w2M4775wDDjgghx12WN7+9rfnda97XU466aTMmDEjb3nLW3Leeedl3333rcjwjHwaRwAAAJXT6o4jAADW0qAaR//1X/+Vvfba60Wh0T9661vfmje84Q0566yzkiRvetObMm3atPzud78b3KSskzSOAAAAKmfC2P7gqF1wBADAIA0qOHrwwQczZcqUlz03ZcqU/OlPfyr/fdttt01bW9tgXpJ1VPPo/tKbxhEAAMDaa22yqg4AgLUzqOCoubk5d955Z4rF4irPFIvF3HnnnWlubi6/r6urK+PHjx/MS7KOamzo/xbs7Ol9ye8nAAAAXl5L88CquqUaRwAADM6ggqNZs2blr3/9az7xiU+ko6PjRY93dnbmuOOOy1//+tcV7jP6y1/+kqlTpw5+WtY5pTuOisVk2fK+Gk8DAADUygUXXJAtt9wyjY2NmTFjhjXngzShub9x1N4pOAIAYHDqB/OkM888M7/61a/yjW98Iz/60Y+y//77lwOhefPm5Ze//GUWLVqUjTfeOP/xH/+RpH+93UMPPZTPfOYzlZueEa8UHCX99xw1/sPfAQCA9cOPf/zjnHDCCbnwwgszY8aMnHPOOdlvv/3y0EMPZZNNNqn1eCNKa1N/4+j5ZcvTvbwvDfWD+n1RAADWY4MKjrbYYovceuut+djHPpbrr78+P/rRj1505i1veUu+8Y1vZIsttkiSbL311nnyySfT0tKydhOzTqkfVZeGUXXp7u1LZ09vJtR6IAAAYMh99atfzVFHHZUjjjgiSXLhhRfm6quvzsUXX5yTTjqpxtONLOObRqdQ6N/q0N7Zk43Hjan1SAAAjDCDCo6S5JWvfGV+9atf5W9/+1tuueWWPPnkk0mSyZMnZ88998wrX/nKFc6PGTMmEydOXLtpWSc1jn4hOAIAANYv3d3dueuuu3LyySeX31dXV5dZs2bltttuW+lzli1blmXLlpX/vnjx4qrPOVKMqitkfOPotHf2pL2zW3AEAMAaG1Rw9J73vCeTJ0/OBRdckG222SbbbLNNpediPdLUMCqLu5ans1twBAAAtfDII4/koYceyqJFi5IkEyZMyHbbbZctt9yy6q/9zDPPpLe390W/aDhx4sT86U9/WulzzjzzzHz+85+v+mwjVWtzf3C0qMM9RwAArLlBLTu+5ppr8uyzz1Z6FtZTzQ39+aXGEQAADK1LLrkk22+/fbbZZpu87W1vywc+8IF84AMfyNve9rZss8022X777XPppZfWeswXOfnkk9Pe3l5+mzdvXq1HGlZamxuSJG2CIwAABmFQwdFWW22VpUuXVnoW1lONo0clicYRAAAMoQsuuCAf/vCH85a3vCVz587N/Pnz09nZmc7Ozjz55JO5/vrrM2vWrBxxxBH5+te/XrU5Ntpoo4waNSoLFy5c4f0LFy7MpEmTVvqcMWPGZPz48Su88YLWptFJkkUd3TWeBACAkWhQwdEhhxySG2+8MQsWLKj0PKyHmkb3fxt2CI4AAGDInHXWWfnSl76UCy64IPvss08mTZqUMWPGlO+n3XvvvXP++efnP/7jP/Kf//mfVZujoaEhu+22W+bOnVt+X19fX+bOnZuZM2dW7XXXZROa+4Ojdo0jAAAGYVDB0cknn5y99tore++9d6644or09PjHKINXWlXXZVUdAAAMmQULFmTXXXd92XO77rrri9pAlXbCCSfkoosuyve+9708+OCD+fjHP56lS5fmiCOOqOrrrqtKq+o0jgAAGIz6wTxpu+22S19fX+bNm5f/9//+XwqFQjbZZJM0Nja+6GyhUMjf/va3tR6UdVd5VZ3gCAAAhsxuu+2Ws88+O3vuuWfGjBmz0jPLli3LOeeck913372qsxx00EF5+umnc9ppp2XBggWZPn165syZk4kTJ1b1dddVrQONo7ZOv+QJAMCaG1Rw9Mgjj6zw92KxaG0dg9bU4I4jAAAYauedd1723XffbLHFFnn729+e7bffPq2trUmS9vb2/OlPf8o111yTvr6+XHfddVWf59hjj82xxx5b9ddZH5TuOLKqDgCAwRhUcNTX11fpOViPNWscAQDAkJs+fXr++Mc/5hvf+EauvfbaXH311Vm0aFGSZMKECXnVq16VY445Jh/72Mey0UYb1Xha1oRVdQAArI1BBUdQSRpHAABQGxtttFFOPfXUnHrqqbUehQoqr6rTOAIAYBDqaj0AuOMIAACgckqNozaNIwAABmGtgqNrr702Bx54YDbddNOMGTMmRx55ZPmxX/7ylznhhBMyf/78tR6SdVvTQHDUoXEEAADDzpw5c7L11lvXegzWwIRS46hT4wgAgDU36ODouOOOy9ve9rb87Gc/y5IlS9LT05NisVh+fPLkyTnnnHPy4x//uCKDsu5qHlhV16VxBAAAw87SpUvz6KOP1noM1kBrU3/jqKO7N8uW+zkLAIA1M6g7jr7//e/nvPPOy+67755vfetbmT59eurqVsygdt5550ydOjX/+7//m09+8pMVGZZ1U6M7jgAAYMiddtppq3XuwQcfrPIkVNq4xvrUFZK+YtLe0ZNNxo+q9UgAAIwgg2ocfeMb30hra2uuvvrqTJ8+fZXndt555/z9738f7Gxr5IILLsiWW26ZxsbGzJgxI7/73e9e8vxPf/rTbL/99mlsbMxOO+2Ua665ZoXHi8ViTjvttEyePDlNTU2ZNWtW/vKXv6xw5rnnnsuhhx6a8ePHp7W1NUceeWSef/75Fc7cf//92WuvvdLY2JipU6fmK1/5SmU+4XVIeVWdxhEAAAyZM844I+eff35+8IMfvOTbTTfdVOtRWUN1dYW0NFlXBwDA4AwqOHrggQey5557ZuONN37Jcy0tLVm4cOGgBlsTP/7xj3PCCSfk9NNPz913351p06Zlv/32y1NPPbXS87feemsOOeSQHHnkkbnnnnsye/bszJ49Ow888ED5zFe+8pWce+65ufDCC3P77bdn7Nix2W+//dLV1VU+c+ihh+YPf/hDrrvuulx11VW56aabcvTRR5cfX7x4cfbdd99sscUWueuuu/Kf//mf+bd/+7d861vfqt4XYwQqr6rTOAIAgCGzzTbbZPbs2Xn44Ydf8u2CCy6o9agMQmtz/7q6RUu7azwJAAAjzaDvOCoUCi97Zv78+WlqahrsS6y2r371qznqqKNyxBFHZIcddsiFF16Y5ubmXHzxxSs9/7WvfS37779/PvOZz+TVr351vvCFL2TXXXfN+eefn6S/bXTOOefklFNOybvf/e7svPPO+f73v5/58+fnyiuvTNK/rmHOnDn59re/nRkzZuQNb3hDzjvvvFx22WWZP39+kuSSSy5Jd3d3Lr744rzmNa/JwQcfnH/5l3/JV7/61ap/TUaSUuOoU+MIAACGzMyZM3Prrbe+7LlCobDCfbaMDK3NGkcAAAzOoIKjbbfdNnfffXd6elb9D9AlS5bk3nvvzWte85pBD7c6uru7c9ddd2XWrFnl99XV1WXWrFm57bbbVvqc2267bYXzSbLffvuVzz/88MNZsGDBCmdaWloyY8aM8pnbbrstra2t2X333ctnZs2albq6utx+++3lM2984xvT0NCwwus89NBDWbRo0Vp+5uuOxtKquu7lNZ4EAADWH8cdd1z++Z//+WXP7b333vn1r389BBNRSa2lVXUdGkcAAKyZQQVH73vf+/Lkk0/mpJNOWuWZk08+Oe3t7Tn44IMHPdzqeOaZZ9Lb25uJEyeu8P6JEydmwYIFK33OggULXvJ86b8vd2aTTTZZ4fH6+vpssMEGK5xZ2cf4x9f4v5YtW5bFixev8LauK6+q6+mr8SQAALD+2G233fIv//IvL3tuo402yt577z0EE1FJpVV1bR0aRwAArJlBBUfHH398dtppp5xzzjmZOXNmvvSlLyVJ/va3v+Xss8/OG9/4xnz961/PLrvskqOOOqqiA6/rzjzzzLS0tJTfpk6dWuuRqq6pwao6AACASrKqDgCAwRpUcNTU1JRf/epX2X///XP77bfnX//1X5Mkv/nNb/KpT30qN998c9761rfmF7/4xQpr2qpho402yqhRo7Jw4cIV3r9w4cJMmjRppc+ZNGnSS54v/fflzjz11FMrPL58+fI899xzK5xZ2cf4x9f4v0pNrdLbvHnzVv6Jr0PKdxx1C44AAAAqobWp1Diyqg4AgDVTP9gnbrzxxrn66qtz33335dprr80jjzySvr6+bLbZZnnrW9+aPfbYo5JzrlJDQ0N22223zJ07N7Nnz06S9PX1Ze7cuTn22GNX+pyZM2dm7ty5Of7448vvu+666zJz5swkyVZbbZVJkyZl7ty5mT59epJk8eLFuf322/Pxj3+8/DHa2tpy1113ZbfddkuSXH/99enr68uMGTPKZ/71X/81PT09GT16dPl1tttuu0yYMGGls40ZMyZjxoxZq6/JSPOPjaO+vmLq6go1nggAANZN3//+91frXENDQzbccMNMmzbtRSu6GRkmjC3dcaRxBADAmhl0cFQybdq0TJs2rRKzDNoJJ5yQD33oQ9l9992zxx575JxzzsnSpUtzxBFHJEkOP/zwbLrppjnzzDOT9F8Cu/fee+ess87KAQcckMsuuyx33nlnvvWtbyVJCoVCjj/++JxxxhnZdttts9VWW+XUU0/NlClTyuHUq1/96uy///456qijcuGFF6anpyfHHntsDj744EyZMiVJ8oEPfCCf//znc+SRR+azn/1sHnjggXzta1/L2WefPfRfpGGs1DhKkmXL+8pBEgAAUFkf/vCHUyis/i9qFQqFzJo1K+edd1623XbbKk5GpbU09QdHizSOAABYQ2sdHA0HBx10UJ5++umcdtppWbBgQaZPn545c+Zk4sSJSZLHHnssdXUvbOXbc889c+mll+aUU07J5z73uWy77ba58sors+OOO5bPnHjiiVm6dGmOPvrotLW15Q1veEPmzJmTxsbG8plLLrkkxx57bN7ylrekrq4u733ve3PuueeWH29pacm1116bY445Jrvttls22mijnHbaaTn66KOH4KsycjT+Q3DU2dMrOAIAgCo57bTT8sgjj+T73/9+XvGKV2TffffN5ptvniSZN29err322ixZsiSHHXZYxowZk1tvvTXXXntt9tprr9x1113ZdNNNa/wZsLomNJdW1WkcAQCwZgrFYrE42Cc//PDD+c1vfpMnn3wyy5YtW/kLFAo59dRTBz3g+m7x4sVpaWlJe3t7xo8fX+txquZVp/wi3cv7cvNn35TNJjTXehwAAGpoffk3cC387W9/yx577JEDDzwwZ511VlpaWlZ4fPHixTnhhBNyxRVX5Pbbb8/WW2+dz3zmMzn77LNzzDHH5LzzzqvR5C/N98yL3f94W951/i2Z3NKY205+S63HAQCggqr9799BBUfd3d35p3/6p1xyySVJkpf6EIVCIb29vYOfcD23vvwANP3fr01bR09+dcIb88pNxtV6HAAAamh9+TdwLbz//e/P3XffnT//+c8rbGX4R319fXnVq16VXXfdNT/5yU/S3d2drbbaKs3NzfnLX/4yxBOvHt8zL/bYsx1543/+Oo2j6/KnL7yt1uMAAFBB1f7376BW1Z122mn54Q9/mNbW1nzwgx/Mq171qowb53/sZ/CaRo9KW3rS2d1X61EAAGCd9etf/zr77rvvKkOjJKmrq8see+yRa6+9NknS0NCQadOm5YYbbhiiKamE1rH9dxx19fSlq6d3hRXhAADwUgYVHF166aVpbW3NPffcky222KLSM7Eeahr4Iaaje3mNJwEAgHVXR0dHFixY8LLnFi5cmK6urvLfx48fn/r6deKK3PXGuDH1GT2qkJ7eYp5avCybb2glOAAAq2fVv2b2Ep566qnstddeQiMqpqmhPzjq7LHWEAAAqmWnnXbKTTfdlJtuummVZ37zm9/kxhtvzE477VR+37x587LxxhsPxYhUSKFQyKsm9m8GeWB+e42nAQBgJBlUcCQwotJKjaMuwREAAFTNiSeemN7e3uy333756Ec/muuuuy5/+tOf8qc//SnXXXddPvaxj2W//fZLsVjMiSeemCRpb2/PXXfdlde97nU1np41NW1qa5LkvnltNZ0DAICRZVC7Bj7ykY/ki1/8Yp5++mm/dUZFlBpHHd2CIwAAqJb3vOc9Ofvss/PZz342F110Ub797W+v8HixWExDQ0POPvvsHHjggUmSZ599Np///Ofzlre8pRYjsxambdaSS29P7nu8rdajAAAwggwqOPrMZz6Te+65J29605ty3nnnZZ999kmhUKj0bKxHSo0jq+oAAKC6jjvuuLzrXe/Kf//3f+fWW2/Nk08+mSSZPHlyXv/61+eII47I1ltvXT6/9dZb57Of/WytxmUtlBpHv3+8Pb19xYyq83M7AAAvb1DB0Stf+cokyaOPPppZs2Zl9OjRmTRpUurqXrz5rlAo5G9/+9vaTck6r3zHkcYRAABU3VZbbZUzzjij1mNQZdtuMi7NDaOytLs3f3/6+Ww7cOcRAAC8lEEFR4888sgKf+/u7s5jjz1WiXlYT7njCAAAoLJG1RWy45SW/O6R53LvvDbBEQAAq2VQwVFfX1+l52A91zjaHUcAADCUbrvttvzmN7/JE088kSTZdNNNs9dee2XmzJk1noxKmja1Pzi67/G2vG/3qbUeBwCAEWBQwRFUWnODO44AAGAo/PnPf85hhx2WO++8M0lSLBaTpHxv7e67754f/vCH2XbbbWs2I5Wz82atSZL7H2+v7SAAAIwYgiOGBavqAACg+p588snsvffeWbhwYaZMmZL3ve992XLLLVMoFPLII4/kpz/9ae64447ss88+ufPOOzN58uRaj8xamj61NUny4JOLs2x5b8bUj6rtQAAADHt1q3PoIx/5SC6++OKVPvbzn/88995770ofO/3007PbbrsNejjWH00NVtUBAEC1nXHGGVm4cGE++clP5u9//3vOPvvsHHfccfmXf/mXfPWrX83f//73nHDCCXnyySfzxS9+sdbjUgGbTWjKBmMb0tNbzINPLqn1OAAAjACrFRx997vfzc0337zSx2bPnp1zzz13pY899thjqwyV4B+VgqNOwREAAFTNNddck+222y5nnXVWGhoaXvT46NGj85//+Z/ZbrvtctVVV9VgQiqtUChk581akiT3zWur7TAAAIwIqxUcQbWVVtW54wgAAKrnySefzK677vqSZwqFQnbdddc8+eSTQzQV1TZt4J4jwREAAKtDcMSwUA6ONI4AAKBqxo8fn3nz5r3suXnz5mX8+PFDMBFDYdrUgcbR4221HQQAgBFBcMSwUF5Vp3EEAABVM3PmzNxyyy25+uqrV3nmmmuuyS233JI999xzCCejmnYeaBz97emlWdzVU9thAAAY9gRHDAtW1QEAQPWddNJJqaury4EHHpjDDjssv/jFL/Lggw/mwQcfzJw5c/KhD30oBx54YOrq6nLSSSfVelwqZKNXjMlmE5qSJA883l7jaQAAGO7qaz0AJP/QOLKqDgAAqmbmzJn5zne+k49+9KO55JJLcumll67weLFYTFNTU775zW/mda97XY2mpBqmbdaaxxd15t7H27LnKzeq9TgAAAxjgiOGhWar6gAAYEh88IMfzD777JOLLrooN998c+bPn58kmTJlSvbaa68ceeSRmTp1ao2npNKmTW3J1b9/MvfNa6v1KAAADHOrHRx973vfy/e+970Xvb9QKKzyMVhdjaM1jgAAYKhsttlm+fznP1/rMRhCpXuO7reqDgCAl7HadxwVi8VBvcHqKN1xtGx5X/r6fN8AAABU0k6btqSukDzZ3pWnFnfVehwAAIax1Woc9fX1VXsO1nOlO46S/nV1Y8fYoggAAGtr6623HvRzC4VC/va3v1VwGmpp7Jj6bLvJuDy0cEnue7w9b92hsdYjAQAwTPlf5xkWGusFRwAAUGmPPPJIrUdgGNl5s5b+4GheW966w8RajwMAwDC12qvqoJrq6gppHN3/7eieIwAAqIy+vr61emPdMm1qa5LkvsfbajoHAADDm+CIYaN0z1Fnj+AIAACg0qZt1pokuW9emzuJAQBYJcERw0ZzQ/96Oo0jAACAyttu0rg01NdlcdfyPPJsR63HAQBgmBIcMWyUV9VpHAEAAFRcQ31dXjNlfJLkfuvqAABYBcERw0ZTw8CqOo0jAACAqiitq7t3XltN5wAAYPgSHDFsNI8eWFWncQQAAFAV06a2JEnuf7y9xpMAADBcCY4YNho1jgAAAKpq54HG0QNPtKent6+2wwAAMCwJjhg2mgbuOOrQOEqSFIvFWo8AAACsY7bacGzGNdZn2fK+PLRgSa3HAQBgGBIcMWw0N/SvquvSOEpvXzGzv35r3nHeb/LXp56v9TgAAMA6oq6uUL7nyLo6AABWRnDEsNE4emBVncZRHn12ae6b15YHnlic2RfckjkPPFnrkQAAgHXEzpv133N037y22g4CAMCwJDhi2GgSHJU9+mxH+c/PL1uej/3w7pz5iwez3A5yAABgLU2b2pokue/xtprOAQDA8CQ4Ythoauj/duy0qi4PP7M0SfLWHSbmqL22SpJ888a/5/CLf5dnn19Wy9EAAIARrrSq7s8Ll6Sje3lthwEAYNgRHDFslO44Ehz1r6pLkm02fkX+9YAdcsEHdk1zw6jc+rdn847zbs69VkoAAACDNKmlMRPHj0lfMXngicW1HgcAgGFGcMSw4Y6jFzwysKpuq42akyQH7Dw5Pzvm9dl647F5sr0r77/wtlx6+2MpFou1HBMAABihSq2j+62rAwDg/xAcMWyU7jjq0DjKIwONoy02HFt+37YTx+Vnx7w++79mUrp7+/K5K36fE/+/+9MlaAMAANZQ6Z4j2wwAAPi/BEcMG80N/cHR+h6E9PT25fFFnUmSLf8hOEqScY2j840P7prP7r996grJT+96PP/vwlsz77mOWowKAACMUC80jtprOwgAAMOO4Ihhw6q6fk8s6kxvXzGNo+uyybgxL3q8UCjk4/tskx8cOSMbjG3IA08szjvPvzk3/vnpGkwLAACMRDtt1pIkeey5jjy3tLvG0wAAMJwIjhg2mhqsqkteWFO35YZjU1dXWOW5179yo/zvJ96QaZu1pK2jJx/+zu9y3ty/pK/PvUcAAMBLa2kana036t9wcJ97jgAA+AeCI4YNq+r6PfJM6X6j5pc9u2lrU37ysZn5wIzNUywmZ1335xz9gzvT3tlT7TEBAIARrnTP0f3zrKsDAOAFgiOGjabSqrr1vnHUf1/R/73faFXG1I/KFw/cKV/5fzunob4uv3rwqbz7/JvzpwWLqzkmAAAwwu08sK5O4wgAgH8kOGLYcMdRv0efLTWOVi84Knn/7lNz+cf2zKatTXnk2Y7MvuCW/OzeJ6oxIgAAsA4oN44eb0uxaOU1AAD9BEcMG6VVdet74+jRUuNoo5dfVfd/7bRZS676xBuy17YbpaunL8dddm/+7ed/SPfyvkqPCQAAjHA7TB6f+rpCnnm+O0+0ddZ6HAAAhgnBEcNGaVVdd29flveun0HH8t6+PPbcmq2q+78mjG3Id4/YI5948yuTJN+99ZF84KLfZuHirorNCQAAjHyNo0dl+8njkiT3uecIAIABgiOGjaaBxlGSdK2nDZn5bV1Z3lfMmPq6TBrfOOiPM6qukE/tu10uOnz3jBtTnzsfXZR3nHdzfvfwcxWcFgAAGOmmbdaapH9dHQAAJIIjhpEx9XUpFPr/3NG9vLbD1Mgj5fuNmlNXV1jrj/fWHSbm5594Q7abOC5PL1mWD1z021x888P2lwMAAEleCI7unddW0zkAABg+BEcMG4VCobyurqt7/WwcPVoOjga3pm5lttpobK44Zs+8a9qULO8r5t+v+mOOu+ze9TacAwAAXjBtamuS5IEn2tPb5xfMAAAQHDHMlIKjzp7eGk9SGw8/U7rfqLmiH7e5oT5fO3h6Tn/nDqmvK+Tn983PgRfcmoefWVrR1wEAAEaWV27yijQ3jMrS7t787ennaz0OAADDgOCIYaVxIDhaX9sw1WgclRQKhRzx+q3yo6Nfl43HjclDC5fkXefdnGv/sKDirwUAAIwMo+oK2XHTliTW1QEA0E9wxLDS3LB+N45KdxxttVHlg6OS1265Qa7+xBvy2i0nZMmy5Tn6B3flP3/5J2spAABgPTV9YF3d/Y+31XQOAACGB8ERw0rTQHDUtR4GR719xcx7rjNJskWFV9X9X5uMb8ylR70uH95zyyTJBb/+Wz78nd/luaXdVX1dAABg+Nl5s/7G0X3z2ms8CQAAw4HgiGHlhVV1619wNL+tM929fWkYVZfJLU1Vf73Ro+ryb+96Tb528PQ0jR6V3/zlmbzzvJuzcHFX1V8bAAAYPqZt1pok+dOCxevlL/EBALAiwRHDSnlV3XoYHD36bEeSZOoGTRlVVxiy13339E1zxTF7ZvMNmvNEW2eOueTu9PT2DdnrAwAAtbXZhKZsMLYhPb3FPPjk4lqPAwBAjQmOGFaaRq+/q+qG4n6jVdl+0vh87yN7ZNyY+tz56KJ88ZoHh3wGAACgNgqFQqaV19W11XYYAABqTnDEsFIKjjrXx+Domf7gaIsNhz44SvoDq7PePy1J8p1bHsnP75tfkzkAAIChN21qa5Lk/sfdcwQAsL4THDGsNDasv3ccPTKwqm7LDZtrNsO+r5mUj++zTZLkpMvvz18WLqnZLAAAwNAp3XN07+NtNZ0DAIDaExwxrDSvx42jRwdW1W1Zg1V1/+hTb31V9txmw3R09+ajP7wrS7p6ajoPAABQfTsPrKr7+9NLs9jPAAAA6zXBEcNK00DjqGs9axz19RXz6HOlxlFtg6P6UXU595BdMml8Y/7+9NJ85qf3p1gs1nQmAACgujZ8xZhsNqEpSfJ76+oAANZrgiOGlcbR6+equicXd6V7eV9GjypkcktjrcfJRq8Yk69/cNeMHlXInD8syEW/+XutRwIAAKqsdM/RvfPaajoHAAC1JThiWGluWD9X1T36TP+auqkTmlM/anj8v+Wum0/Iae/YIUny5TkP5bd/f7bGEwEAANU0feCeo/vdcwQAsF4bHv8LNQxoGmgcda1nwdEjzw6sqavx/Ub/1wdft0UO3GXT9PYVc+yl92Th4q5ajwQAAFRJ6Z6j++ZZVQcAsD4THDGslO44Wt9W1T3ybH/jaIsNm2s8yYoKhUK+eOBO2X7SuDzz/LIcc8nd6entq/VYAABAFey4aUvqCsmCxV1+aQwAYD0mOGJYKTWO1rdVdY8MrKrbcsPh1ThK+sO8b3xwt4wbU587H12UL17zYK1HAgAAqmDsmPpsu8m4JMl97jkCAFhvCY4YVkqNo871rHH06MCquuHWOCrZaqOxOev905Ik37nlkfzs3idqPBEAAFAN06YOrKtzzxEAwHpLcMSwsj42jvr6inn0uf7G0VbD7I6jf7Tvaybln/fZJkly0uW/z58XLqnxRAAAQKVNm9qaJLn/cfccAQCsrwRHDCvrY+No4ZKudPX0pb6ukE1bm2o9zkv61L7b5fWv3DCdPb352A/uypKunlqPBAAAVNC0zVqT9K+qKxaLtR0GAICaEBwxrKyPjaNHnulfU7fZhKbUjxre/y85qq6Qcw/eJZNbGvP3Z5bmMz+93w+TAACwDtlu0rg01NdlcdfyPDKwUhsAgPXL8P5fqVnvlBpHXetRcPTos/1r6rbYcPiuqftHG75iTL5+6K4ZPaqQOX9YkIt+8/dajwQAAFTI6FF1ec2U8Un6W0cAAKx/BEcMK6XGUU9vMT29fTWeZmiUfotvON9v9H/tsvmEnPaOHZIkX57zUH7792drPBEAAFAppXV19wqOAADWS4IjhpVS4yhZf9bVPfJMqXHUXONJ1swHX7dFDtxl0/T2FXPspXdnQXtXrUcCAAAqYPrU1iTJ/Y+31XQOAABqQ3DEsNIwqi51hf4/d3WvJ8HRwKq6LUfIqrqSQqGQLx64U7afNC7PPN+dYy69O93L14+WGAAArMt23qwlSfKH+YvXm00QAAC8QHDEsFIoFMrr6jrWg+CoWCzm0YFVdVuOoFV1JU0No3LhB3fLuMb63PXoonzxmgdrPRIAALCWttxwbMY31mfZ8r48tGBJrccBAGCICY4Ydpoa6pOsH6vqnl6yLJ09vRlVV8imrU21HmdQttxobL76/ulJku/e+kh+du8TtR0IAABYK3V1hew8cM/RfdbVAQCsdwRHDDtNDf3flutDcPTwwP1Gm7Y2paF+5P6/41t3mJh/3mebJMlJl/8+f17otxIBAGAkmza1f13dffPaajsIAABDbuT+L9Wss0qr6jrXg1V1pTV1W2zYXONJ1t6n9t0ur3/lhuns6c3HfnBXlnT11HokAABgkKYNNI7uf7y9toMAADDkBEcMO+VVdetBcPTIs/2No61G4P1G/9eoukLOPXiXTG5pzN+fWZrP/PT+FIvFWo8FAAAMwrSprUmSPy9cko7u5bUdBgCAISU4YthpGr3+rKorBUdbbDjyg6Mk2fAVY/L1Q3fN6FGFzPnDglz0m7/XeiQAAGAQJo5vzKTxjekrJg88sbjW46zUlfc8kZlnzs291ukBAFSU4IhhZ31aVffIM/2r6rZcB1bVleyy+YSc9s7XJEm+9Is/5ba/PVvjiQAAgMHYebPhe89RR/fyfOGqP+bJ9q5cftfjtR4HAGCdIjhi2GkurapbxxtHxWIxj65jjaOSD87YPO/ZZdP0FZNP/OjuLGjvqvVIAADAGiqtq7v38baazrEyl/z2sTy7tDtJcv8wnA8AYCQTHDHsNJYaR+t4cPTM891Z2t2bukIydYOmWo9TUYVCIf9x4E7ZftK4PPN8d4659O50L++r9VgAAMAamD4QHA23YKazuzffvOlv5b8/+OQSP28AAFSQ4Ihhp6lh4I6jdXxVXel+oymtTRlTP6rG01ReU8OoXPjB3TKusT53PbooX7zmwVqPBAAArIEdN+1fVTfvuc48+/yyGk/zgkt/91ieeb47m01o+v/bu/P4qOqz///vmSQzScgyELIQsgBubBIWW4x6CyoKilaE2moR0aJWC73d6tYFra237V2tVYva1gr9Vf0qfr/FVtxKWWtFwEBAVrVAAoEkkpCEkD3z+f0xCwSSkIQk50zyej4eeTQ58zlnrkkPcT65cl2XPNERqmv0amehPecwAQAAhCISR7CdqF5ScbT3kC9xNKiHtak73qD+ffSbb42WJC36eK/+lltgbUAAAAAA2iw+KkJDEn37lS0F5RZH41NT36iXVvuqjeZdcqZGpXkk2XMOEwAAQKgicQTbiQrMOOrhFUd5JVWSpEH9oy2OpGtdPjxZcy85Q5L08P/7TJ8XHbE4IgAAAABtlWWzxMwb6/P11ZFaDfREafrYNGWl+aqiNu+3R2ILAACgJyBxBNvpNRVHJT2/4ijgvsvP0UVn9ld1faPu/EuOjtTUWx0SAAAAgDYIJmZskDiqqW/Ui/5qo+9fcoZc4c5gxZHd5jABAACEMhJHsJ2oiN414yizFySOwpwOPXvDaKXGR2r3oaN64K0tMsZYHRYAAACAU8hK90iStuwvt/w9/Fuf7lNRRa0GxEfqm+PSJB1LbH1RXKnK2gYrwwMAAOgxSBzBdqIDrep6cMWRMUZ5h/yt6hJ6dqu6gIQYtxbMHKuIMIc+2FaoP6zZbXVIAAAAAE5h2IA4hTsdKjlap/2Hqy2Lo7ahUS+s8lUb3TXxDLnDfZ0qkuIiNSA+UsZIW20yhwkAACDUkTiC7US6/K3qenDFUenROh2pbZDDIaX36x2JI0kak9FX868ZIUn61Qc7tfY/JRZHBAAAAKA1kRFhGjYgTpKv6sgq/y+nQAfLa5Qc59a3zktv8tgof9UR7eoAAAA6B4kj2E5gxlFVD644CrSpS42PUqT/9fYWN43P0PQxA+U10rzXN+rJ93borU/3afO+MlXV0VoCAAAAsJtAYmazRYmZugavFqz8UpJ054QzTtpDBeYcbbYwsQUAANCThFsdAHCiaH/FUU0Prjja629Tl9lL2tQdz+Fw6InrztX2gxXaWXhEvz+hZV1a3yidnRyrs5JjdHZSrM5OjtUZSX2CLQwBAAAAdK+sdI9eW5evzfvKLHn+JZv2q6CsWomxbt349YyTHh/tn8NkVXwAAAA9Db+Jhe0E/nqsJ884yvNXHGUm9LE4EmtEucL01p3Z+vvmA/q88Ig+L6rUF8VHdKjS1zd9/+FqrdhZHFzvcPgTSkmxOis5Vmcnx/gSSokxinL1rootAAAAoLsFEjOfFZSr0WsU5nR023PXN3r1O3+10fcuHtJsx4aRA30VUfsPV6ukslYJMe5uiw8AAKAnInEE2wm2quvJFUclvoqjwf17X8VRQGxkhGaOz2xyrPRonT4vOqIvio7oi+JK/+eVKjlap32l1dpXWq3lJySUMvpF66ykmGBC6aykWJ2ZFNPrWgACAAAAXeWMxBhFu8JUVdeoL4srdU5KbLc999ubCrSvtFr9Y1wn7R8C4qMiNKR/H+0+dFRbCsp1yTlJ3RYfAABAT0TiCLYTqCCp6cEVR3t7ecVRS/r1cen8IQk6f0hCk+MllbXBqqRAMumL4kqVHq1TXkmV8kqq9M8dxxJKzkBCKTlWZyXFBFvfnZFIQgkAAABorzCnQ+cOjNe6PaXavL+s2xJHDY3HZhvdcfGQVrsNZKV7fImjfSSOAAAATheJI9hOYMZRdX2jjDFyOLqvDUJ3MMZozyFf4mgQiaM2SYhxKzvGrewzmiaUDlXWBhNJwf8tPqKyqnrtLanS3pIqLdteFFzvdEg3Zw/SY98Y0d0vAQAAAAhpWekeX+JoX5m+dV56tzzn3zcf0N6SKvXr49JN5zdfbRQwKi1eSzYVaPP+sm6JDQAAoCcjcQTbCVSENHqN6huNXOE9K3FUVlWvIzUNknxVMei4/jFu9Y9x64Iz+gePGWP0VWWtvvQnkz4vrtQXRb45SuXV9fJER1gYMQAAABCastI8kqQt+8u75fkavUa/W+GrNrr9v4Yo2tX6ry9GBeMr65F/gAgAANCdSBzBdqKOayVWXdcoV7jTwmg6X6BN3YD4yFZbLaBjHA6HkmIjlRQbqQvOPCGhdKRWzm4c5AsAAAD0FFnp8ZKkHQcrVFPf2OUtoJduOaDdh47KEx2hWdmtVxtJ0ojUOIU7HTpUWacD5TUa6Inq0vgAAAB6spD/jXxpaalmzpypuLg4eTwezZkzR5WVla2eU1NTo7lz5yohIUExMTGaMWOGioqKmqzJz8/X1KlTFR0draSkJD3wwANqaGhosmbVqlUaO3as3G63zjzzTC1atKjJ42vWrNE111yj1NRUORwOvf32253xkns8V7hT4f5f7lf3wDlHx+YbUW3UnRwOh5LiItU/xm11KAAAAEDIGeiJUkIflxq8RtsPVnTpczV6jZ73VxvddtFgxbhP/TevkRFhwdlLm/eVdWV4AAAAPV7IJ45mzpypbdu2admyZVq6dKnWrFmjO+64o9Vz7r33Xr3zzjt66623tHr1ah04cEDTp08PPt7Y2KipU6eqrq5OH3/8sf785z9r0aJFmj9/fnDNnj17NHXqVF1yySXKzc3VPffco9tuu00ffvhhcM3Ro0eVlZWlBQsWdP4L7+ECVUc9MnF0qEoS840AAAAAhA6Hw6GsdI8kaUsXJ2be++ygviyuVFxkuGZfMKjN5wXa1THnCAAA4PSEdKu6HTt26IMPPtCGDRt03nnnSZKef/55XXXVVXrqqaeUmpp60jnl5eX605/+pNdff12XXnqpJGnhwoUaNmyYPvnkE51//vn6xz/+oe3bt+uf//ynkpOTNXr0aP385z/XQw89pMcee0wul0svvfSSBg8erKefflqSNGzYMH300Ud65plnNHnyZEnSlVdeqSuvvLKbvhs9S6QrTEdqG1RV13DqxSEmz19xNKg/iSMAAAAAoWNUWrxW7CzW5i6cc+T1Gj2/4gtJ0pyLhig2su0zSrPS4vV/1ktb9nXPHCYAAICeKqQrjtauXSuPxxNMGknSpEmT5HQ6tW7dumbPycnJUX19vSZNmhQ8NnToUGVkZGjt2rXB65577rlKTk4Orpk8ebIqKiq0bdu24JrjrxFYE7hGR9XW1qqioqLJR28U7Z/9U9MTK45KAhVHtKoDAAAAEDoCFUddWdHz4bZCfV5UqdjIcN1y4aB2nRuoOPqsoFxer+n84AAAAHqJkE4cFRYWKikpqcmx8PBw9evXT4WFhS2e43K55PF4mhxPTk4OnlNYWNgkaRR4PPBYa2sqKipUXV3d4df05JNPKj4+PviRnp7e4WuFsmCrujqvxZF0vmMzjqg4AgAAABA6svyJmd1fHVV5dX2nX9/rNXp2ua/a6NYLBys+qu3VRpJ0dnKMIiOcqqxt0O5Drc8+BgAAQMtsmTh6+OGH5XA4Wv3YuXOn1WF2iUceeUTl5eXBj3379lkdkiUi/YmjntaqrqyqTmVVvg1WJhVHAAAAAEJIvz4upfeLkiR91gXt6pbtKNLOwiOKcYdrzoWD231+eJhTI1PjJUmbaVcHAADQYbaccXT//ffrlltuaXXNkCFDlJKSouLi4ibHGxoaVFpaqpSUlGbPS0lJUV1dncrKyppUHRUVFQXPSUlJ0fr165ucV1RUFHws8L+BY8eviYuLU1RU1ClfY0vcbrfcbneHz+8pAq3qqntYq7o8f5u65Di3ol22/OcHAAAAAC3KSvNoX2m1Nu8v00Vn9e+06xpj9Jy/2uiWCwYpPrp91UYBo9I8+jTvsLbsL9OMcWmdFh8AAEBvYsvfXCcmJioxMfGU67Kzs1VWVqacnByNGzdOkrRixQp5vV6NHz++2XPGjRuniIgILV++XDNmzJAk7dq1S/n5+crOzg5e94knnlBxcXGwFd6yZcsUFxen4cOHB9e89957Ta69bNmy4DVwegKt6nrajCPa1AEAAAAIZVlpHi3dclCb95V16nWX7yjWtgMV6uMK05yL2l9tFJCV7qs4yu2CiigAAIDewpat6tpq2LBhmjJlim6//XatX79e//73vzVv3jzdcMMNSk1NlSQVFBRo6NChwQqi+Ph4zZkzR/fdd59WrlypnJwc3XrrrcrOztb5558vSbriiis0fPhwzZo1S5s3b9aHH36on/zkJ5o7d26wGujOO+/U7t279eCDD2rnzp164YUXtHjxYt17773B+CorK5Wbm6vc3FxJ0p49e5Sbm6v8/Pxu/C6FpshAxVFdD0scHfJVHA2iTR0AAACAEJSV7pEkbenExIwxRs+t8FUb3XzBIPXt4+rwtQJzmHYcqFBdQ8+bmQsAANAdQjpxJEmvvfaahg4dqssuu0xXXXWVLrroIv3hD38IPl5fX69du3apqqoqeOyZZ57R1VdfrRkzZujiiy9WSkqK/vrXvwYfDwsL09KlSxUWFqbs7GzddNNNuvnmm/X4448H1wwePFjvvvuuli1bpqysLD399NN6+eWXNXny5OCaTz/9VGPGjNGYMWMkSffdd5/GjBmj+fPnd+W3pEcIVBxV9bCKozwqjgAAAACEsJED4+R0SIUVNSosr+mUa67a9ZW27C9XVESYbjuNaiPJN0s2PipCdY1e7So80inxAQAA9Da2bFXXHv369dPrr7/e4uODBg2SMabJscjISC1YsEALFixo8bzMzMyTWtGdaOLEidq0aVOrj5/43GibwIyjmp5WceRPHA3uT+IIAAAAQOiJdoXr7ORY7Sw8os37y5QS3/x84bYyxuhZ/2yjm7MzlRBzejN/HQ6HRqXF619fHNLm/WU6Ny3+tK4HAADQG4V8xRF6pkDFUXUPqzjaW+KrfMukVR0AAACAEBVoB7dlf9lpX2vNF4eUu69MkRFO3fZfQ077etKx+Dp7DhMAAEBvQeIIthQZaFXXgyqOyqvrVXq0ThKt6gAAAACErlHpviqezftOb86RMUbP/vNzSdLM8ZlKjD29aqOAUf4qo86cwwQAANCbkDiCLQVa1fWkiqN8f7VRYqxbMe6Q7xIJAAAAoJc6vuLI6+14e/aP/1Oijfllcoc79b2LO6faSJKy0j2SpC+Kj6iqrqHTrgsAANBbkDiCLUUFZhz1oMRRYL7RINrUAQAAAAhh56TEyh3uVEVNQ3Cf016+aiPfbKMbv56hpLjITosvOS5SKXGR8hppa0FFp10XAACgtyBxBFvqia3q9h7ybahoUwcAAAAglEWEOTUiNU6StLmDc44+2V2q9XtL5Qp36q6JZ3RidD6BdnXMOQIAAGg/EkewpWCrup6UOPK3qqPiCAAAADjmiSee0AUXXKDo6Gh5PB6rw0EbBdrBdXTO0bPLfbONbvhaupI7sdooIBhfBxNbAAAAvRmJI9hSVETPa1WXF2hV15+KIwAAACCgrq5O119/ve666y6rQ0E7BOYcdSQxs253iT7ZXaqIMIfunND51UbSsYqjLfs7ltgCAADozcKtDgBoTlRPbFUXnHFE4ggAAAAI+NnPfiZJWrRokbWBoF0CFT3bDlSovtGriLC2/13q8yu+lCR967x0pXqiuiI8jRrokSTll1ap9Gid+vVxdcnzAAAA9ERUHMGWogKt6npIxdGRmnodqqyTJGXQqg4AAAA4LbW1taqoqGjyge41KCFacZHhqmvwalfhkTafl5NXqo++PKSIMEeXzDYKiI+O0GB/t4cttKsDAABoFxJHsKVA4qintKrL8883SujjUlxkhMXRAAAAAKHtySefVHx8fPAjPT3d6pB6HYfDEaw6yt1X1ubznl3uqzb65rg0pfXt2j+qo10dAABAx5A4gi0FWtVV95BWdYHEEfONAAAA0Bs8/PDDcjgcrX7s3Lmzw9d/5JFHVF5eHvzYt29fJ0aPtgrMOWprRc+m/MNa8/lXCnM69P2JZ3ZdYH6j2hkfAAAAfJhxBFsKzjiqb5QxRg6Hw+KITk9gvlEmbeoAAADQC9x///265ZZbWl0zZMiQDl/f7XbL7XZ3+Hx0jkBFz+Z9bavoeW75F5Kk6WMGKr1f1++NRqf74svdV94j9pUAAADdhcQRbCnQqs4YqbbBq0h/IilU7T3kSxwNSqDiCAAAAD1fYmKiEhMTrQ4DXWy0v1XdF8VHdLS2QX3cLf+KYfO+Mq3c5as2mndp11cbSdLwAfEKczp0qLJWB8trlOqJ6pbnBQAACHW0qoMtHZ8o6glzjgKt6qg4AgAAAJrKz89Xbm6u8vPz1djYqNzcXOXm5qqystLq0HAKSXGRSomLlNdIWwtarzp6foWv2uja0anK7KY/qItyhens5FhJtKsDAABoDxJHsKWIMKciwnxtBKp6wJyjQKu6wcw4AgAAAJqYP3++xowZo0cffVSVlZUaM2aMxowZo08//dTq0NAGWf52cJtbScxsLSjXP3cUy+mQ5l3SPdVGAaOD8bWtnR4AAABIHMHGAnOOqkO84uhobYOKj9RKkjL7kTgCAAAAjrdo0SIZY076mDhxotWhoQ2y/O3qWkvMBGYbfSMrVUMSY7ojrKBRaR5JvlZ5AAAAaBsSR7CtwJyj6hCvOAq0qesbHaH46AiLowEAAACAzpN1isTM9gMV+sf2Ijkc0rxLz+q+wPxGpfkqjj7bXy6v13T78wMAAIQiEkewrZ5ScZTnb1M3iDZ1AAAAAHqYc/2Jmf2Hq1VSWXvS44HZRlePStWZSd1bbSRJZyfHyh3u1JHaBu3x780AAADQOhJHsK0oV7ik0K84CmxOBnXTAFgAAAAA6C5xkREakujb62w5oV3drsIjen9roRwO6QeXdu9so4CIMKdGDvTPOaJdHQAAQJuQOIJtRUX4bs+Qrzg65GtVl5kQbXEkAAAAAND5Rvvb1eWekJgJVBtdNXKAzk6O7eaojgm0qzsxsQUAAIDmkTiCbfWUGUd7qTgCAAAA0INlpXskSVv2lwWPfVF0RO9+dlCS9IPLrKk2CgjOYTouPgAAALSMxBFsKyrC36ou1CuOSnwVR8w4AgAAANATBSp6Nu8vlzFGkvS7lV/KGGnKiBQNTYmzMrxgfNsOVKi+0WtpLAAAAKGAxBFsqydUHFXXNaqwokaSNIhWdQAAAAB6oGED4hQR5lDp0TrtP1yt/3xVqXc2H5BkfbWR5Ov+EBcZrroGr3YVHrE6HAAAANsjcQTb6gkzjvJKfW3q4qMi5Il2WRwNAAAAAHS+yIiwYFXR5v1lWrDiS3mNNGlYskakxlscneR0OjSKdnUAAABtRuIIthUVEfoVR3sP+dvUUW0EAAAAoAfLSvcliP6We0Bv5xZIku6+7CwrQ2oi0K5uy75yiyMBAACwPxJHsK0oV+jPOMor8VUcMd8IAAAAQE+W5a/oWba9SF4jXTo0SeemWV9tFJCV7pFExREAAEBbkDiCbQUrjkI4cbTXnzjKTCBxBAAAAKDnCiRmAv7bRtVG0rHE1udFR1RV12BtMAAAADZH4gi2FeXyzziiVR0AAAAA2NoZiTHq4/L98d+EsxM1+oREktVS4iOVFOuW10jbDlRYHQ4AAICtkTiCbQVb1YVw4ohWdQAAAAB6gzCnQ5cPT1ZkhFP3XX621eE0a5S/6mjzvjJL4wAAALA7EkewrVBvVVdT36gD5TWSpEG0qgMAAADQwz11fZbWPTLppLZ1djE63TdzafP+cosjAQAAsLdwqwMAWhJMHIVoxVF+qa9NXWxkuPpGR1gcDQAAAAB0rfAwp+Kj7fv3qYGKoy37yyyNAwAAwO7s+44OvV60K7QrjvYe8repS+gjh8NhcTQAAAAA0LuNSvNVHOWVVKmsqs7iaAAAAOyLxBFsKzLEW9XllfgqjphvBAAAAADW80S7NCghWpK0hXZ1AAAALSJxBNuKcoV2q7o9JYGKo2iLIwEAAAAASMfa1W3eV2ZpHAAAAHZG4gi2FRXyFUe+xFFmAhVHAAAAAGAHgXZ1m6k4AgAAaBGJI9hWdIhXHO095G9VR8URAAAAANhCVrpHkrR5f5mMMdYGAwAAYFMkjmBbx884CrU39LUNjTpQXi2JGUcAAAAAYBcjUuMU5nToqyO1KqyosTocAAAAWyJxBNsKzDiSpJp6r4WRtN++0ioZI8W4w5XQx2V1OAAAAAAASdGucJ2VFCNJ2ryPdnUAAADNIXEE2wrMOJJCb85RoE1dZkK0HA6HxdEAAAAAAAKy0jySpC37yyyNAwAAwK5IHMG2wpwOucJ9t2jIJY5KjkqSBiXQpg4AAAAA7GRUerwkact+Ko4AAACaQ+IIthaoOqqua7A4kvbJK/FVHA3qH21xJAAAAACA4wUqjjbvL5PXG1rzdAEAALoDiSPYWrQrkDgKrRlHgYqjTCqOAAAAAMBWzkmJlTvcqSM1DcG9GwAAAI4hcQRbC1Yc0aoOAAAAANAJIsKcGp4aJ4l2dQAAAM0hcQRbi/QnjqpCqFVdXYNXBYerJdGqDgAAAADs6Ph2dQAAAGiKxBFsLdCqriaEKo72Ha6S1/hiT4xxWx0OAAAAAOAEWenxkqTN+8qsDQQAAMCGSBzB1qJcodeqLu+4+UYOh8PiaAAAAAAAJxrlrzjadqBC9Y2hNVMXAACgq5E4gq0da1UXOomjvYeqJEmDEmhTBwAAAAB2NDihj2Ld4apt8OrzoiNWhwMAAGArJI5ga1H+xFF1CCWOAhVHg/r3sTgSAAAAAEBznE6HRgXb1ZVbHA0AAIC9kDiCrYXijKM9JVQcAQAAAIDdBdrVbdlfZmkcAAAAdkPiCLYWaFUXqjOOAAAAAAD2lJXmrzjaT8URAADA8UgcwdaiXKE146i+0av9h6slSYNIHAEAAACAbQUqjj4vOhJS7dEBAAC6Gokj2Fp0RGi1qis4XK1Gr1FkhFPJcW6rwwEAAAAAtGBAfKQSY91q9BptO0DVEQAAQACJI9haoOIoVP76a4+/Td2ghD5yOBwWRwMAAAAAaInD4aBdHQAAQDNIHMHWAjOOQqVVXd6hwHyjaIsjAQAAAACcSqBd3Zb9ZZbGAQAAYCckjmBr0YGKoxBpVbe3pEqSNKg/840AAAAAwO6y0j2SpM37yiyNAwAAwE5IHMHWokJsxlHeca3qAAAAAAD2Nmqgr1Xd3pIqlVfVWxwNAACAPZA4gq1FukKrVV2g4ohWdQAAAABgf337uJTRz7d/21JQZm0wAAAANkHiCLYWHRE6reoaGr3aV+pvVUfFEQAAAACEhFFpvqqjLfvLLY4EAADAHkgcwdai/BVHNSFQcXSgrEYNXiN3uFMpcZFWhwMAAAAAaIPR/jlHucw5AgAAkETiCDYXFUIVR3v8840yE6LldDosjgYAAAAA0Baj0jySpC37yyyNAwAAwC5IHMHWIiNCZ8ZRXjBxRJs6AAAAAAgVIwfGyemQiipqVVRRY3U4AAAAliNxBFuL9reqq23wyus1FkfTur2HAvONoi2OBAAAAADQVtGucJ2dHCtJ2ky7OgAAABJHsLfAjCNJqmmwd9VRoOJoUH8qjgAAAAAglIxKi5ckbaZdHQAAAIkj2Ftk+LHEkd3b1QVmHA2iVR0AAAAAhJRjc47KrQ0EAADABkgcwdacTociI3y3abWNE0eNXqN9pb5WdZm0qgMAAACAkJLlTxxt3lcmY+zdJh0AAKCrkTiC7UVF+KqOaurtmzg6UFat+kYjV5hTA+KjrA4HAAAAANAO56TEyhXuVEVNg/aWVFkdDgAAgKVIHMH2AokjO7eqy/NvLDISohXmdFgcDQAAAACgPVzhTg0fECdJ2sKcIwAA0MuROILtRbl8iaNqG1ccHZtvRJs6AAAAAAhFWWnxkqTN+5hzBAAAejcSR7C9UEgc5R3yJY4yE/pYHAkAAAAAoCNG+eccUXEEAAB6OxJHsL1Aq7pqG7eqC/TAHtSfxBEAAAAAhKKsdI8kaeuBcjU0eq0NBgAAwEIkjmB7Ua5wSXZPHNGqDgAAAABC2ZD+fRTrDldNvVefF1VaHQ4AAIBlSBzB9qIifLepXVvVNXqN8gMVR7SqAwAAAICQ5HQ6NHKgb84R7eoAAEBvRuIIthdoVVdj08RRYUWN6hq9ighzaEB8pNXhAAAAAAA6KNCubvP+cmsDAQAAsBCJI9helMuXOKqyaau6vEO+NnXp/aIVHsY/KQAAAAAIVVlpvoqjzfvKrA0EAADAQvyWG7YXFeGfcWTTiqM9wflGtKkDAAAAgFA2yl9xtKvoiC26XhhjrA4BAAD0QiSOYHtRLv+MI7tWHPnnG2UmRFscCQAAAADgdKTGR6p/jEuNXqNtByosjWVj/mF9/X+W66dvb7U0DgAA0PuQOILtBWYc2TVxtPcQFUcAAAAA0BM4HA5lpXkkWduublfhEd26cIO+OlKr/5uzXw2NXstiAQAAvQ+JI9helMvereoCFUeD+pM4AgAAAIBQN8qfONqyv8yS599XWqVZf1qn8up6Sb698M7CI5bEAgAAeicSR7C9YMWRDRNHXq/R3uCMI1rVAQAAAECoG5UeL0nasr+825/7qyO1mvWndSo+UquzkmI0JsMjSdqUf7jbYwEAAL0XiSPYnp1nHBUdqVFtg1fhTocGeqKsDgcAAAAAcJoCrep2HzoarPrpDuXV9br5lfXaW1KltL5R+suc8ZpwdqIkKSePxBEAAOg+JI5ge1ER9m1Vt/eQr01der9ohYfxzwkAAAAAQl2/Pi6l9/P9YeBn3VR1VF3XqNv+vEE7Dlaof4xLf5kzXinxkRqb0VeStDG/rFviAAAAkEgcIQREufyt6mxYcZTnb1OXSZs6AAAAAOgxAnOONnfDnKP6Rq/mvr5RG/YeVqw7XH/+7tc12D9Dd3SGRw6HlF9apa+O1HZ5LAAAABKJI4QAO8842hOcb9TH4kgAAAAAAJ0lKy0w56isS5/H6zV64K3NWrGzWO5wp/50y9c0IjU++HhcZITOToqVJG1kzhEAAOgmJI5ge8HEkR0rjvyt6qg4AgAAAICeIzDnaPO+rmtVZ4zR40u36+3cAwpzOvTiTWP19cH9Tlo3NtMXC4kjAADQXUgcwfaCrepsWHG0N1Bx1J+KIwAAAADoKUYOjJfTIRVW1Ki4oqZLnuO55V9q0cd7JUlPX5+lS4cmN7suOOcoj8QRAADoHiSOYHt2TRwZY5RX4qs4olUdAAAAAPQcfdzhOjMpRpK0eX/nVx39+eO9euafn0uSHrtmuKaNGdji2rGZvsTRlv3lqmvwdnosAAAAJyJxBNsLtKqra/Cq0WssjuaY4iO1qq5vVJjToYGeKKvDAQAAAAB0olH+dnWdPefob7kFevTv2yRJd192lm65cHCr64f07yNPdIRqG7zacbCiU2MBAABoDokj2F60v+JIslfV0d5DvjZ1Az1RcoXzTwkAAAAAepKsdI8kKXdfWaddc+XOYt2/eLMkaXZ2pu6ZdNYpz3E4HMF2dTm0qwMAAN2A33bD9tzHJWWq6+yTOAq2qWO+EQAAAAD0OFlp8ZKkzwrKZczpd7/YsLdUd72Wowav0bWjU/XoNSPkcDjadO7YDI8kaWM+iSMAAND1SBzB9hwOR7BdnZ0SR3tKfBVHgxKiLY4EAAAAANDZhqbEyRXmVFlVvfJLq07rWtsPVOi7izaopt6rS85J1FPXZ8npbFvSSDo252gjFUcAAKAbkDhCSAi0q7NTq7o8f+IoM4GKIwAAAADoaVzhTg1LjZMkbd5f3uHr7D10VDe/sl5Hahr0tUF99cLMcYoIa9+vY7LSPHI6pAPlNSosr+lwLAAAAG1B4gghITLCfomjvYf8reqoOAIAAACAHinQrm5zB+ccFVXU6KY/rdOhyloNTYnVy7O/pqjj5vi2VR93uIam+JJYtKsDAABdjcQRQkLgjXVVXYPFkfgYY4IVR8w4AgAAAICeaVSaR5K0ZX9Zu88tq6rTzX9ar/2Hq5WZEK3/b87XFR8V0eFYxvnb1eXQrg4AAHQxEkcICYFWdTU2qTj6qrJWR+sa5XRIaX2jrA4HAAAAANAFAhVHWwsq1NDobfN5VXUN+u6iDdpVdERJsW69Ome8kmIjTyuWsZkeSVQcAQCArkfiCCEh2Kquru1v1LtSXomvTV2qJ0ru8Pa3GQAAAAAA2N+QxBjFuMNVXd+oL4or23ROXYNX3/tLjjbmlyk+KkJ/mTNe6f1Ov8X52AxfxdG2ggrb/FElAADomUgcISRERdirVd3eQ742dYNpUwcAAAAAPVaY06GRA32zhdrSrq7Ra3Tf4lz964tDiooI0yu3fE3npMR2SiwZ/aLVP8alukavth0o75RrAgAANIfEEUJCIHFkl7+q2uufb5SZcPp/NQYAAAAAsK8s/5yjzftbT9YYYzT/b1u1dMtBRYQ59NKsccG5RJ3B4XBojL/qaGNeWaddFwAA4EQhnzgqLS3VzJkzFRcXJ4/Hozlz5qiysvXy8ZqaGs2dO1cJCQmKiYnRjBkzVFRU1GRNfn6+pk6dqujoaCUlJemBBx5QQ0PTapdVq1Zp7NixcrvdOvPMM7Vo0aImjz/55JP62te+ptjYWCUlJWnatGnatWtXp7zu3iYw46jaNokjX6u6QQlUHAEAAABATzbKnzg6VcXR0//4XK+ty5fDIf3mW6M14ezETo8lkIjKyWPOEQAA6DohnziaOXOmtm3bpmXLlmnp0qVas2aN7rjjjlbPuffee/XOO+/orbfe0urVq3XgwAFNnz49+HhjY6OmTp2quro6ffzxx/rzn/+sRYsWaf78+cE1e/bs0dSpU3XJJZcoNzdX99xzj2677TZ9+OGHwTWrV6/W3Llz9cknn2jZsmWqr6/XFVdcoaNHj3b+N6KHi3TZbcZRoOKIxBEAAAAA9GRZ6fGSpJ0Hj7TYBePlf+3W71Z+KUn6xbSRuiYrtUtiCcw52ph/WMaYLnkOAACAcKsDOB07duzQBx98oA0bNui8886TJD3//PO66qqr9NRTTyk19eQ3auXl5frTn/6k119/XZdeeqkkaeHChRo2bJg++eQTnX/++frHP/6h7du365///KeSk5M1evRo/fznP9dDDz2kxx57TC6XSy+99JIGDx6sp59+WpI0bNgwffTRR3rmmWc0efJkSdIHH3zQ5LkXLVqkpKQk5eTk6OKLL+7Kb02PE5xxVG/9jCNjjPIO+SqOBvenVR0AAAAA9GQDPVFK6ONSydE6bT9YEUzeBPy/nP36xbs7JEkPTD5HM8dndlkso9LiFe50qPhIrQrKqpXWlz0pAADofCFdcbR27Vp5PJ5g0kiSJk2aJKfTqXXr1jV7Tk5Ojurr6zVp0qTgsaFDhyojI0Nr164NXvfcc89VcnJycM3kyZNVUVGhbdu2Bdccf43AmsA1mlNe7uuH3K9fvxbX1NbWqqKioskHjrWqq6mzvlVdydE6HaltkMMh3qQDAAAAQA/ncDg0Ks1XdbRlX1mTx5ZtL9KD/2+LJOm2iwbr+xPP6NJYIiPCNCI1ThLt6gAAQNcJ6cRRYWGhkpKSmhwLDw9Xv379VFhY2OI5LpdLHo+nyfHk5OTgOYWFhU2SRoHHA4+1tqaiokLV1dUnPa/X69U999yjCy+8UCNHjmzxNT355JOKj48PfqSnp7e4tjeJjLDPjKNAm7rU+KhgXAAAAACAnisr3SNJ2rK/PHhs7X9KNPf1jWr0Gs0Ym6YfXTVMDoejy2MZ46942pRf1uXPBQAAeidbJo4efvhhORyOVj927txpdZjtMnfuXG3dulVvvPFGq+seeeQRlZeXBz/27dvXTRHaW7BVnQ0qjvb629RlJlBtBAAAAAC9QVaaR5KUu79MkrS1oFy3/3+fqq7Bq8uHJ+tXM86V09n1SSNJGpfpSxxRcQQAALqKLWcc3X///brllltaXTNkyBClpKSouLi4yfGGhgaVlpYqJSWl2fNSUlJUV1ensrKyJlVHRUVFwXNSUlK0fv36JucVFRUFHwv8b+DY8Wvi4uIUFRXV5Pi8efO0dOlSrVmzRmlpaa2+LrfbLbfb3eqa3ijYqs5GFUeD+vexOBIAAAAAQHcItKrb/dVRbd5Xpu8u2qDK2gadP6Sfnr9xjMLDuu/vcsf6E0c7Dlaouq5RUS46YQAAgM5ly8RRYmKiEhMTT7kuOztbZWVlysnJ0bhx4yRJK1askNfr1fjx45s9Z9y4cYqIiNDy5cs1Y8YMSdKuXbuUn5+v7Ozs4HWfeOIJFRcXB1vhLVu2THFxcRo+fHhwzXvvvdfk2suWLQteQ5KMMfrBD36gJUuWaNWqVRo8eHA7vxMICLwRtkOruj0lvoqjQVQcAQAAAECvkBDj1kBPlArKqnXDHz5RdX2jRg6M0x9vPq/bW5inxkcqOc6toopabdlfpvFDErr1+QEAQM9ny1Z1bTVs2DBNmTJFt99+u9avX69///vfmjdvnm644QalpqZKkgoKCjR06NBgBVF8fLzmzJmj++67TytXrlROTo5uvfVWZWdn6/zzz5ckXXHFFRo+fLhmzZqlzZs368MPP9RPfvITzZ07N1gNdOedd2r37t168MEHtXPnTr3wwgtavHix7r333mB8c+fO1auvvqrXX39dsbGxKiwsVGFhYbMzkNC6SBu1qgtUHGUmUHEEAAAAAL3FaP+co+r6Rg3p30eLbv26YiMjuj0Oh8NxrF1dPu3qAABA5wvpxJEkvfbaaxo6dKguu+wyXXXVVbrooov0hz/8Ifh4fX29du3apaqqquCxZ555RldffbVmzJihiy++WCkpKfrrX/8afDwsLExLly5VWFiYsrOzddNNN+nmm2/W448/HlwzePBgvfvuu1q2bJmysrL09NNP6+WXX9bkyZODa1588UWVl5dr4sSJGjBgQPDjzTff7OLvSs8TaFVXbXHiyBijPYd8iaPBtKoDAAAAgF4j0CJuQHyk/nLbePWPsa7N/NgMXywb88osiwEAAPRctmxV1x79+vXT66+/3uLjgwYNkjGmybHIyEgtWLBACxYsaPG8zMzMk1rRnWjixInatGlTi4+f+LzouKgIe8w4OlxVryM1DZKkjH60qgMAAACA3uI7X89QmEOaNDxZAz1Rpz6hCwWSWJvyD8sYI4fDYWk8AACgZwn5iiP0DnZpVbfX36ZuQHxkt/exBgAAAABYJ8oVplsuHKy0vtb/EeGI1Di5wpw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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"Cu2O\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 100)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", + "\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "7307d306-ba2f-4f94-aaf4-f6be405fb045", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 30x30x30 with error of 0.00435 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "markdown", + "id": "1d33a01a-f278-4498-892e-62f47d7953e4", + "metadata": {}, + "source": [ + "### 8. ZrO2 (3 atoms) semiconductor" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "477d120b-47e4-4cdc-b3d7-da32f1a6e80c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 100.0)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"ZrO2\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 100)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", + "\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 100)" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "2374fec5-d5f2-44f7-9e36-adfce75bd96b", + "execution_count": 76, + "id": "ae0b5b40-2457-45c6-b0bc-fce4b8f7725a", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 46x46x46 with error of 0.00381 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "markdown", + "id": "b7974239-463d-4105-8f53-27c175c5085c", + "metadata": {}, + "source": [ + "### 9. SiO2 (6 atoms) insulator" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "6f61a8d8-71e7-4d70-8602-40ec7686d27a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 100.0)" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"SiO2\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 100)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", + "\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "8ff61223-a6a7-4934-9b9a-83ae2cc2d215", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 26x26x26 with error of 0.00898 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "markdown", + "id": "35ab1c3e-ce49-4a6b-b74d-247a0117d3fe", + "metadata": {}, + "source": [ + "### 10. CsPbBr3 (5 atoms) semiconductor (unconverged)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "490d92b5-69cc-4727-8ecd-47631017d9dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 100.0)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"CsPbBr3\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 100)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", + "\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "d5e0645a-98cd-44e9-93b7-31e2125ded28", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 98x98x98 with error of 0.07701 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] + }, + { + "cell_type": "markdown", + "id": "e20bde15-1e41-48a0-ba57-28057f29736b", + "metadata": {}, + "source": [ + "### 11. Pb (1 atom) metal" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "bce9b494-facb-449f-8215-13e1580395a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 100.0)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = \"Pb\"\n", + "\n", + "data = np.genfromtxt(f'./calcs/{mat}/stats.dat', skip_header = 1)\n", + "\n", + "plt.figure(figsize = (20, 10))\n", + "\n", + "\n", + "ax1 = plt.subplot(121)\n", + "\n", + "\n", + "ax1.plot(data[:, 0], data[:, 1])\n", + "\n", + "ax1.set_ylabel(\"Energy (eV)\", fontsize = 15)\n", + "ax1.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax1.set_xlim(0, 100)\n", + "\n", + "ax2 = plt.subplot(122)\n", + "\n", + "\n", + "ax2.plot(data[:, 0], np.log10(data[:, 2]*1e3))\n", + "ax2.plot(range(101), [-2]*101, ls = '--')\n", + "\n", + "ax2.set_ylabel(\"$\\log_{10}$ (Error)\", fontsize = 15)\n", + "ax2.set_xlabel(\"kpoint side (k x k x k)\", fontsize = 15)\n", + "\n", + "ax2.set_xlim(0, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "415e25fe-2690-4cd3-a01c-b970cfb87247", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of kpoint grid is 38x38x38 with error of 0.004220000000000001 meV\n" + ] + } + ], + "source": [ + "print(f\"Optimal value of kpoint grid is {int(data[-1, 0])}x{int(data[-1, 0])}x{int(data[-1, 0])} with error of {data[-1, -1]*1e3} meV\")" + ] } ], "metadata": { diff --git a/Ladygin/examples/calcs/Al/stats.dat b/Ladygin/examples/calcs/Al/stats.dat new file mode 100644 index 00000000..3b151e95 --- /dev/null +++ b/Ladygin/examples/calcs/Al/stats.dat @@ -0,0 +1,15 @@ +kpoint total_energy error +6 -68.63632933 0.61008725 +10 -68.55263786 0.08369147 +14 -68.56322077 0.01058292 +18 -68.56839651 0.00517574 +22 -68.57192760 0.00353109 +26 -68.57166732 0.00026028 +30 -68.57090364 0.00076369 +34 -68.57025682 0.00064681 +38 -68.57039873 0.00014191 +42 -68.57060703 0.00020830 +46 -68.57071424 0.00010721 +50 -68.57067615 0.00003810 +54 -68.57060717 0.00006898 +58 -68.57059737 0.00000980 diff --git a/Ladygin/examples/calcs/CsPbBr3/stats.dat b/Ladygin/examples/calcs/CsPbBr3/stats.dat new file mode 100644 index 00000000..2f09ff7b --- /dev/null +++ b/Ladygin/examples/calcs/CsPbBr3/stats.dat @@ -0,0 +1,25 @@ +kpoint total_energy error +6 -35906.97897527 0.67779081 +10 -35906.97505166 0.00392361 +14 -35906.98093789 0.00588623 +18 -35906.98106456 0.00012667 +22 -35906.98007964 0.00098492 +26 -35906.98079979 0.00072015 +30 -35906.98011746 0.00068233 +34 -35906.98024876 0.00013129 +38 -35906.98017175 0.00007701 +42 -35906.98007760 0.00009415 +46 -35906.97991324 0.00016436 +50 -35906.98043665 0.00052341 +54 -35906.98034903 0.00008762 +58 -35906.98031910 0.00002993 +62 -35906.98042781 0.00010871 +66 -35906.98026767 0.00016014 +70 -35906.98032196 0.00005429 +74 -35906.98026291 0.00005905 +78 -35906.98042767 0.00016476 +82 -35906.98026822 0.00015946 +86 -35906.98029692 0.00002871 +90 -35906.98028536 0.00001156 +94 -35906.98034250 0.00005714 +98 -35906.98026549 0.00007701 diff --git a/Ladygin/examples/calcs/Cu2O/stats.dat b/Ladygin/examples/calcs/Cu2O/stats.dat new file mode 100644 index 00000000..e152c8ee --- /dev/null +++ b/Ladygin/examples/calcs/Cu2O/stats.dat @@ -0,0 +1,8 @@ +kpoint total_energy error +6 -12729.23964630 0.37376060 +10 -12729.24029312 0.00064681 +14 -12729.24022033 0.00007279 +18 -12729.24003652 0.00018381 +22 -12729.24005692 0.00002041 +26 -12729.24009039 0.00003347 +30 -12729.24009475 0.00000435 diff --git a/Ladygin/examples/calcs/Fe/stats.dat b/Ladygin/examples/calcs/Fe/stats.dat new file mode 100644 index 00000000..bc4994a7 --- /dev/null +++ b/Ladygin/examples/calcs/Fe/stats.dat @@ -0,0 +1,20 @@ +kpoint total_energy error +6 -3369.03816287 1.22715913 +10 -3368.99397416 0.04418870 +14 -3368.98236701 0.01160715 +18 -3368.98939490 0.00702788 +22 -3368.99357252 0.00417763 +26 -3368.99803206 0.00445954 +30 -3368.99643176 0.00160030 +34 -3368.99669557 0.00026381 +38 -3368.99589678 0.00079879 +42 -3368.99399702 0.00189976 +46 -3368.99346871 0.00052831 +50 -3368.99525623 0.00178752 +54 -3368.99511418 0.00014204 +58 -3368.99481309 0.00030109 +62 -3368.99599488 0.00118179 +66 -3368.99595883 0.00003606 +70 -3368.99550344 0.00045538 +74 -3368.99581801 0.00031456 +78 -3368.99576018 0.00005782 diff --git a/Ladygin/examples/calcs/GaAs/stats.dat b/Ladygin/examples/calcs/GaAs/stats.dat new file mode 100644 index 00000000..23d52857 --- /dev/null +++ b/Ladygin/examples/calcs/GaAs/stats.dat @@ -0,0 +1,13 @@ +kpoint total_energy error +6 -6176.40168747 2.35804029 +10 -6176.42675419 0.02506672 +14 -6176.42797299 0.00121880 +18 -6176.42812292 0.00014993 +22 -6176.42827435 0.00015143 +26 -6176.42818401 0.00009034 +30 -6176.42825639 0.00007238 +34 -6176.42823871 0.00001769 +38 -6176.42827803 0.00003932 +42 -6176.42824333 0.00003469 +46 -6176.42827816 0.00003483 +50 -6176.42826959 0.00000857 diff --git a/Ladygin/examples/calcs/MgB2/stats.dat b/Ladygin/examples/calcs/MgB2/stats.dat new file mode 100644 index 00000000..720dadf2 --- /dev/null +++ b/Ladygin/examples/calcs/MgB2/stats.dat @@ -0,0 +1,7 @@ +kpoint total_energy error +6 -184.77495635 1.22318572 +10 -184.77204324 0.00291311 +14 -184.77266638 0.00062314 +18 -184.77257998 0.00008640 +22 -184.77268774 0.00010776 +26 -184.77268597 0.00000177 diff --git a/Ladygin/examples/calcs/NaCl/stats.dat b/Ladygin/examples/calcs/NaCl/stats.dat new file mode 100644 index 00000000..600090f7 --- /dev/null +++ b/Ladygin/examples/calcs/NaCl/stats.dat @@ -0,0 +1,4 @@ +kpoint total_energy error +6 -2569.45366756 0.24168389 +10 -2569.45368634 0.00001878 +14 -2569.45368634 0.00000000 diff --git a/Ladygin/examples/calcs/Ni/stats.dat b/Ladygin/examples/calcs/Ni/stats.dat new file mode 100644 index 00000000..a4011930 --- /dev/null +++ b/Ladygin/examples/calcs/Ni/stats.dat @@ -0,0 +1,14 @@ +kpoint total_energy error +6 -4670.55593664 2.26199880 +10 -4670.53631533 0.01962131 +14 -4670.52788388 0.00843145 +18 -4670.53234818 0.00446430 +22 -4670.53157484 0.00077335 +26 -4670.53090775 0.00066709 +30 -4670.53118000 0.00027225 +34 -4670.53127592 0.00009592 +38 -4670.53118408 0.00009184 +42 -4670.53119755 0.00001347 +46 -4670.53115469 0.00004286 +50 -4670.53119823 0.00004354 +54 -4670.53120476 0.00000653 diff --git a/Ladygin/examples/calcs/Pb/stats.dat b/Ladygin/examples/calcs/Pb/stats.dat new file mode 100644 index 00000000..cd75ca8a --- /dev/null +++ b/Ladygin/examples/calcs/Pb/stats.dat @@ -0,0 +1,10 @@ +kpoint total_energy error +6 -11844.11411830 1.76209003 +10 -11844.21372666 0.09960836 +14 -11844.21814552 0.00441886 +18 -11844.21311712 0.00502839 +22 -11844.21613337 0.00301625 +26 -11844.21624398 0.00011061 +30 -11844.21580221 0.00044178 +34 -11844.21607745 0.00027524 +38 -11844.21607323 0.00000422 diff --git a/Ladygin/examples/calcs/Si/stats.dat b/Ladygin/examples/calcs/Si/stats.dat new file mode 100644 index 00000000..43590741 --- /dev/null +++ b/Ladygin/examples/calcs/Si/stats.dat @@ -0,0 +1,6 @@ +kpoint total_energy error +6 -230.24254976 2.53484585 +10 -230.26588108 0.02333131 +14 -230.26658544 0.00070437 +18 -230.26661714 0.00003170 +22 -230.26661932 0.00000218 diff --git a/Ladygin/examples/calcs/SiO2/stats.dat b/Ladygin/examples/calcs/SiO2/stats.dat new file mode 100644 index 00000000..520f8864 --- /dev/null +++ b/Ladygin/examples/calcs/SiO2/stats.dat @@ -0,0 +1,7 @@ +kpoint total_energy error +6 -2506.40261206 0.06900494 +10 -2506.40499428 0.00238222 +14 -2506.40432991 0.00066437 +18 -2506.40430910 0.00002082 +22 -2506.40419086 0.00011823 +26 -2506.40418188 0.00000898 diff --git a/Ladygin/examples/calcs/ZrO2/stats.dat b/Ladygin/examples/calcs/ZrO2/stats.dat new file mode 100644 index 00000000..d2bc9b3e --- /dev/null +++ b/Ladygin/examples/calcs/ZrO2/stats.dat @@ -0,0 +1,12 @@ +kpoint total_energy error +6 -5319.65254975 0.53713108 +10 -5319.65355766 0.00100791 +14 -5319.65381726 0.00025960 +18 -5319.65416039 0.00034314 +22 -5319.65407781 0.00008259 +26 -5319.65403577 0.00004204 +30 -5319.65398298 0.00005279 +34 -5319.65403441 0.00005143 +38 -5319.65406951 0.00003510 +42 -5319.65403971 0.00002980 +46 -5319.65404352 0.00000381 diff --git a/Ladygin/examples/inputs/CsPbBr3/.ipynb_checkpoints/pw-checkpoint.in b/Ladygin/examples/inputs/CsPbBr3/.ipynb_checkpoints/pw-checkpoint.in index 23f36dca..22888891 100644 --- a/Ladygin/examples/inputs/CsPbBr3/.ipynb_checkpoints/pw-checkpoint.in +++ b/Ladygin/examples/inputs/CsPbBr3/.ipynb_checkpoints/pw-checkpoint.in @@ -22,9 +22,9 @@ conv_thr=1e-13 / ATOMIC_SPECIES -Cs 132.90545 Cs.upf -Pb 207.2 Pb.upf -Br 79.904 Br.upf +Cs 132.90545 Cs.pbe-spn-kjpaw_psl.1.0.0.UPF +Pb 207.2 Pb.pbe-dn-kjpaw_psl.1.0.0.UPF +Br 79.904 Br.pbe-dn-kjpaw_psl.1.0.0.UPF K_POINTS {automatic} 4 4 4 0 0 0 ATOMIC_POSITIONS (crystal) diff --git a/Ladygin/examples/inputs/CsPbBr3/pseudo/.ipynb_checkpoints/Cs.pbe-spn-kjpaw_psl.1.0.0-checkpoint.UPF b/Ladygin/examples/inputs/CsPbBr3/pseudo/.ipynb_checkpoints/Cs.pbe-spn-kjpaw_psl.1.0.0-checkpoint.UPF new file mode 100644 index 00000000..2bf36a0c --- /dev/null +++ b/Ladygin/examples/inputs/CsPbBr3/pseudo/.ipynb_checkpoints/Cs.pbe-spn-kjpaw_psl.1.0.0-checkpoint.UPF @@ -0,0 +1,39658 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.4.1 +Author: ADC +Generation date: 6Jun2019 +Pseudopotential type: PAW +Element: Cs +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 34. Ry + Suggested minimum cutoff for charge density: 258. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 2.0000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 5S 1 0 2.00 1.600 2.100 -1.964306 + 6S 2 0 1.00 1.600 2.100 -0.152961 + 5P 2 1 6.00 1.600 2.300 -0.993117 + Generation configuration: + 5S 1 0 2.00 1.600 2.100 -1.964302 + 6S 2 0 1.00 1.600 2.100 -0.152961 + 6S 2 0 0.00 1.600 2.100 1.000000 + 5P 2 1 6.00 1.600 2.300 -0.993114 + 6P 3 1 0.00 1.600 2.300 6.000000 + 5D 3 2 0.00 1.500 2.100 0.300000 + 5D 3 2 0.00 1.500 2.100 4.050000 + 4F 4 3 0.00 1.500 2.100 0.050000 + 4F 4 3 0.00 1.500 2.100 4.050000 + Pseudization used: troullier-martins + + + &input + title='Cs', + zed=55., + rel=1, + config='[Xe] 6s1 6p0 5d-1 4f-1', + iswitch=3, + dft='PBE' + / + &inputp + lpaw = .true., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='Cs.pbe-spn-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=2.0, + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., + new_core_ps=.true., + rcore=1.6, + tm=.true. + / +9 +5S 1 0 2.00 0.00 1.60 2.10 0.0 +6S 2 0 1.00 0.00 1.60 2.10 0.0 +6S 2 0 0.00 1.00 1.60 2.10 0.0 +5P 2 1 6.00 0.00 1.60 2.30 0.0 +6P 3 1 0.00 6.00 1.60 2.30 0.0 +5D 3 2 0.00 0.30 1.50 2.10 0.0 +5D 3 2 0.00 4.05 1.50 2.10 0.0 +4F 4 3 0.00 0.05 1.50 2.10 0.0 +4F 4 3 0.00 4.05 1.50 2.10 0.0 + + + + + + + 1.657967210099120e-5 1.678821870307313e-5 1.699938849848331e-5 1.721321448293189e-5 + 1.742973006716400e-5 1.764896908218016e-5 1.787096578452252e-5 1.809575486162748e-5 + 1.832337143724565e-5 1.855385107693008e-5 1.878722979359340e-5 1.902354405313488e-5 + 1.926283078013837e-5 1.950512736364176e-5 1.975047166297923e-5 1.999890201369670e-5 + 2.025045723354188e-5 2.050517662852968e-5 2.076309999908378e-5 2.102426764625560e-5 + 2.128872037802136e-5 2.155649951565842e-5 2.182764690020183e-5 2.210220489898209e-5 + 2.238021641224511e-5 2.266172487985544e-5 2.294677428808390e-5 2.323540917648045e-5 + 2.352767464483360e-5 2.382361636021745e-5 2.412328056412712e-5 2.442671407970412e-5 + 2.473396431905262e-5 2.504507929064758e-5 2.536010760683634e-5 2.567909849143421e-5 + 2.600210178741590e-5 2.632916796470356e-5 2.666034812805278e-5 2.699569402503794e-5 + 2.733525805413768e-5 2.767909327292239e-5 2.802725340634455e-5 2.837979285513331e-5 + 2.873676670429486e-5 2.909823073171935e-5 2.946424141689649e-5 2.983485594974047e-5 + 3.021013223952607e-5 3.059012892393711e-5 3.097490537822862e-5 3.136452172450440e-5 + 3.175903884111127e-5 3.215851837215129e-5 3.256302273711400e-5 3.297261514062936e-5 + 3.338735958234376e-5 3.380732086692002e-5 3.423256461416322e-5 3.466315726927411e-5 + 3.509916611323108e-5 3.554065927330304e-5 3.598770573369449e-5 3.644037534632434e-5 + 3.689873884174062e-5 3.736286784017198e-5 3.783283486271872e-5 3.830871334268426e-5 + 3.879057763704923e-5 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0.000000000000000e0 0.000000000000000e0 0.000000000000000e0 0.000000000000000e0 + 0.000000000000000e0 0.000000000000000e0 0.000000000000000e0 0.000000000000000e0 + 0.000000000000000e0 0.000000000000000e0 0.000000000000000e0 + + + + diff --git a/Ladygin/examples/inputs/CsPbBr3/pseudo/Br.upf b/Ladygin/examples/inputs/CsPbBr3/pseudo/Br.pbe-dn-kjpaw_psl.1.0.0.UPF similarity index 100% rename from Ladygin/examples/inputs/CsPbBr3/pseudo/Br.upf rename to Ladygin/examples/inputs/CsPbBr3/pseudo/Br.pbe-dn-kjpaw_psl.1.0.0.UPF diff --git a/Ladygin/examples/inputs/CsPbBr3/pseudo/Cs.pbe-spn-kjpaw_psl.1.0.0.UPF b/Ladygin/examples/inputs/CsPbBr3/pseudo/Cs.pbe-spn-kjpaw_psl.1.0.0.UPF new file mode 100644 index 00000000..2bf36a0c --- /dev/null +++ b/Ladygin/examples/inputs/CsPbBr3/pseudo/Cs.pbe-spn-kjpaw_psl.1.0.0.UPF @@ -0,0 +1,39658 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.4.1 +Author: ADC +Generation date: 6Jun2019 +Pseudopotential type: PAW +Element: Cs +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 34. Ry + Suggested minimum cutoff for charge density: 258. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 2.0000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 5S 1 0 2.00 1.600 2.100 -1.964306 + 6S 2 0 1.00 1.600 2.100 -0.152961 + 5P 2 1 6.00 1.600 2.300 -0.993117 + Generation configuration: + 5S 1 0 2.00 1.600 2.100 -1.964302 + 6S 2 0 1.00 1.600 2.100 -0.152961 + 6S 2 0 0.00 1.600 2.100 1.000000 + 5P 2 1 6.00 1.600 2.300 -0.993114 + 6P 3 1 0.00 1.600 2.300 6.000000 + 5D 3 2 0.00 1.500 2.100 0.300000 + 5D 3 2 0.00 1.500 2.100 4.050000 + 4F 4 3 0.00 1.500 2.100 0.050000 + 4F 4 3 0.00 1.500 2.100 4.050000 + Pseudization used: troullier-martins + + + &input + title='Cs', + zed=55., + rel=1, + config='[Xe] 6s1 6p0 5d-1 4f-1', + iswitch=3, + dft='PBE' + / + &inputp + lpaw = .true., + use_xsd=.FALSE., + pseudotype=3, + 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Dal Corso v.6.4.1 -Author: ADC -Generation date: 6Jun2019 -Pseudopotential type: PAW -Element: Cs -Functional: SLA PW PSX PSC - Suggested minimum cutoff for wavefunctions: 34. Ry - Suggested minimum cutoff for charge density: 258. Ry - The Pseudo was generated with a Scalar-Relativistic Calculation - Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 2.0000 - Pseudopotential contains additional information for GIPAW reconstruction. - Valence configuration: - nl pn l occ Rcut Rcut US E pseu - 5S 1 0 2.00 1.600 2.100 -1.968593 - 6S 2 0 1.00 1.600 2.100 -0.152875 - 5P 2 1 6.00 1.600 2.300 -0.995261 - Generation configuration: - 5S 1 0 2.00 1.600 2.100 -1.968589 - 6S 2 0 1.00 1.600 2.100 -0.152876 - 6S 2 0 0.00 1.600 2.100 1.000000 - 5P 2 1 6.00 1.600 2.300 -0.995258 - 6P 3 1 0.00 1.600 2.300 6.000000 - 5D 3 2 0.00 1.500 2.100 0.300000 - 5D 3 2 0.00 1.500 2.100 4.050000 - 4F 4 3 0.00 1.500 2.100 0.050000 - 4F 4 3 0.00 1.500 2.100 4.050000 - Pseudization used: troullier-martins - - - &input - title='Cs', - zed=55., - rel=1, - config='[Xe] 6s1 6p0 5d-1 4f-1', - iswitch=3, - dft='PBESOL' - / - &inputp - lpaw = .true., - use_xsd=.FALSE., - pseudotype=3, - file_pseudopw='Cs.pbesol-spn-kjpaw_psl.1.0.0.UPF', - author='ADC', - lloc=-1, - rcloc=2.0, - which_augfun='PSQ', - rmatch_augfun_nc=.true., - nlcc=.true., - new_core_ps=.true., - rcore=1.6, - tm=.true. - / -9 -5S 1 0 2.00 0.00 1.60 2.10 0.0 -6S 2 0 1.00 0.00 1.60 2.10 0.0 -6S 2 0 0.00 1.00 1.60 2.10 0.0 -5P 2 1 6.00 0.00 1.60 2.30 0.0 -6P 3 1 0.00 6.00 1.60 2.30 0.0 -5D 3 2 0.00 0.30 1.50 2.10 0.0 -5D 3 2 0.00 4.05 1.50 2.10 0.0 -4F 4 3 0.00 0.05 1.50 2.10 0.0 -4F 4 3 0.00 4.05 1.50 2.10 0.0 - - - - - - - 1.657967210099120e-5 1.678821870307313e-5 1.699938849848331e-5 1.721321448293189e-5 - 1.742973006716400e-5 1.764896908218016e-5 1.787096578452252e-5 1.809575486162748e-5 - 1.832337143724565e-5 1.855385107693008e-5 1.878722979359340e-5 1.902354405313488e-5 - 1.926283078013837e-5 1.950512736364176e-5 1.975047166297923e-5 1.999890201369670e-5 - 2.025045723354188e-5 2.050517662852968e-5 2.076309999908378e-5 2.102426764625560e-5 - 2.128872037802136e-5 2.155649951565842e-5 2.182764690020183e-5 2.210220489898209e-5 - 2.238021641224511e-5 2.266172487985544e-5 2.294677428808390e-5 2.323540917648045e-5 - 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verbosity='high' + verbosity='low' tstress=.true. tprnfor=.true. / @@ -13,18 +13,18 @@ nat=5 ntyp=3 ecutwfc= 40 - A = 5.874 + celldm(1) = 11.100251309530327 / &electrons diagonalization='david' mixing_mode='plain' mixing_beta=0.7 - conv_thr=1e-13 + conv_thr=1e-8 / ATOMIC_SPECIES -Cs 132.90545 Cs.upf -Pb 207.2 Pb.upf -Br 79.904 Br.upf +Cs 132.90545 Cs.pbe-spn-kjpaw_psl.1.0.0.UPF +Pb 207.2 Pb.pbe-dn-kjpaw_psl.1.0.0.UPF +Br 79.904 Br.pbe-dn-kjpaw_psl.1.0.0.UPF K_POINTS {automatic} 4 4 4 0 0 0 ATOMIC_POSITIONS (crystal) diff --git a/Ladygin/examples/inputs/Cu2O/.ipynb_checkpoints/pw-checkpoint.in b/Ladygin/examples/inputs/Cu2O/.ipynb_checkpoints/pw-checkpoint.in new file mode 100644 index 00000000..52efd176 --- /dev/null +++ b/Ladygin/examples/inputs/Cu2O/.ipynb_checkpoints/pw-checkpoint.in @@ -0,0 +1,39 @@ +&control + calculation = 'scf' + prefix = 'Cu2O' + outdir = '/tmp/' + pseudo_dir = '../pseudos/' + verbosity = 'high' + wf_collect = .true. +/ + +&system + ibrav = 1 + celldm(1) = 8.1027922452 + nat = 6 + ntyp = 2 + ecutwfc = 60 + ecutrho = 244 +/ + +&electrons + mixing_mode = 'plain' + mixing_beta = 0.7 + conv_thr = 1.0e-13 +/ + +ATOMIC_SPECIES + Cu 63.546 Cu.pbe-dn-kjpaw_psl.1.0.0.UPF + As 15.999 O.pbe-n-kjpaw_psl.1.0.0.UPF + +ATOMIC_POSITIONS +Cu 0.250000 0.250000 0.750000 +Cu 0.750000 0.250000 0.250000 +Cu 0.250000 0.750000 0.250000 +Cu 0.750000 0.750000 0.750000 +O 0.500000 0.500000 0.500000 +O 0.000000 0.000000 0.000000 + + +K_POINTS {automatic} + 8 8 8 0 0 0 diff --git a/Ladygin/examples/inputs/Cu2O/pseudo/Cu.pbe-dn-kjpaw_psl.1.0.0.UPF b/Ladygin/examples/inputs/Cu2O/pseudo/Cu.pbe-dn-kjpaw_psl.1.0.0.UPF new file mode 100644 index 00000000..1339b60a --- /dev/null +++ b/Ladygin/examples/inputs/Cu2O/pseudo/Cu.pbe-dn-kjpaw_psl.1.0.0.UPF @@ -0,0 +1,20389 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.3 +Author: ADC +Generation date: 6Sep2018 +Pseudopotential type: PAW +Element: Cu +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 45. Ry + Suggested minimum cutoff for charge density: 236. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.9000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 4S 1 0 1.50 1.300 2.100 -0.380951 + 4P 2 1 0.00 1.300 2.100 -0.067542 + 3D 3 2 9.50 1.300 1.800 -0.522124 + Generation configuration: + 4S 1 0 1.50 1.300 2.100 -0.380945 + 4S 1 0 0.00 1.300 2.100 6.100000 + 4P 2 1 0.00 1.300 2.100 -0.067537 + 4P 2 1 0.00 1.300 2.100 6.300000 + 3D 3 2 9.50 1.300 1.800 -0.522111 + 3D 3 2 0.00 1.300 1.800 -0.200000 + Pseudization used: troullier-martins + + + &input + title='Cu', + zed=29., + rel=1, + config='[Ar] 4s1.5 4p0 3d9.5', + iswitch=3, + dft='PBE' + / + &inputp + lpaw = .true., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='Cu.pbe-dn-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=1.9, + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., 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0.000000000000000e0 + + + + diff --git a/Ladygin/examples/inputs/Cu2O/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF b/Ladygin/examples/inputs/Cu2O/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF new file mode 100644 index 00000000..c463e348 --- /dev/null +++ b/Ladygin/examples/inputs/Cu2O/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF @@ -0,0 +1,9766 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.3 +Author: ADC +Generation date: 6Sep2018 +Pseudopotential type: PAW +Element: O +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 47. Ry + Suggested minimum cutoff for charge density: 323. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.1000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 2S 1 0 2.00 1.000 1.300 -1.761150 + 2P 2 1 4.00 0.900 1.350 -0.663754 + Generation configuration: + 2S 1 0 2.00 1.000 1.300 -1.761144 + 2S 1 0 0.00 1.000 1.300 1.000000 + 2P 2 1 4.00 0.900 1.350 -0.663748 + 2P 2 1 0.00 0.900 1.350 0.050000 + Pseudization used: troullier-martins + + + &input + title='O', + zed=8., + rel=1, + config='[He] 2s2 2p4', + iswitch=3, + dft='PBE' + / + &inputp + lpaw = .true., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='O.pbe-n-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=1.1 + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., + new_core_ps=.true., + rcore=0.7, + tm=.true. + / +4 +2S 1 0 2.00 0.00 1.00 1.30 0.0 +2S 1 0 0.00 1.00 1.00 1.30 0.0 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b/Ladygin/examples/inputs/Cu2O/pw.in @@ -0,0 +1,39 @@ +&control + calculation = 'scf' + prefix = 'Cu2O' + outdir = '/tmp/' + pseudo_dir = '../pseudos/' + verbosity = 'high' + wf_collect = .true. +/ + +&system + ibrav = 1 + celldm(1) = 8.1027922452 + nat = 6 + ntyp = 2 + ecutwfc = 60 + ecutrho = 244 +/ + +&electrons + mixing_mode = 'plain' + mixing_beta = 0.7 + conv_thr = 1.0e-13 +/ + +ATOMIC_SPECIES + Cu 63.546 Cu.pbe-dn-kjpaw_psl.1.0.0.UPF + O 15.999 O.pbe-n-kjpaw_psl.1.0.0.UPF + +ATOMIC_POSITIONS +Cu 0.250000 0.250000 0.750000 +Cu 0.750000 0.250000 0.250000 +Cu 0.250000 0.750000 0.250000 +Cu 0.750000 0.750000 0.750000 +O 0.500000 0.500000 0.500000 +O 0.000000 0.000000 0.000000 + + +K_POINTS {automatic} + 8 8 8 0 0 0 diff --git a/Ladygin/examples/inputs/GaAs/pseudo/As.pbe-n-kjpaw_psl.1.0.0.UPF b/Ladygin/examples/inputs/GaAs/pseudo/As.pbe-n-kjpaw_psl.1.0.0.UPF new file mode 100644 index 00000000..ea35a837 --- /dev/null +++ b/Ladygin/examples/inputs/GaAs/pseudo/As.pbe-n-kjpaw_psl.1.0.0.UPF @@ -0,0 +1,12307 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.3 +Author: ADC +Generation date: 6Sep2018 +Pseudopotential type: PAW +Element: As +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 20. Ry + Suggested minimum cutoff for charge density: 103. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + L component and cutoff radius for Local Potential: 2 2.0000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 4S 1 0 2.00 1.600 1.800 -1.065287 + 4P 2 1 3.00 1.600 2.000 -0.381819 + Generation configuration: + 4S 1 0 2.00 1.600 1.800 -1.065283 + 4S 1 0 0.00 1.600 1.800 0.400000 + 4P 2 1 3.00 1.600 2.000 -0.381817 + 4P 2 1 0.00 1.600 2.000 0.400000 + 4D 3 2 -2.00 2.000 2.000 0.500000 + Pseudization used: troullier-martins + + + &input + title='As', + zed=33., + rel=1, + config='[Ar] 4s2 4p3 3d10 4d-1', + iswitch=3, + dft='PBE' + / + &inputp + lpaw = .true., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='As.pbe-n-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=2, + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., + new_core_ps=.true., + rcore=1.5, + tm=.true. + / +5 +4S 1 0 2.00 0.00 1.60 1.80 0.0 +4S 1 0 0.00 0.40 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Dal Corso v.5.1 + Author: ADC + Generation date: 10Oct2014 + Pseudopotential type: PAW + Element: Ga + Functional: SLA PW PBX PBC + + Suggested minimum cutoff for wavefunctions: 60. Ry + Suggested minimum cutoff for charge density: 244. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.9000 + Pseudopotential contains additional information for GIPAW reconstruction. + + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 4S 1 0 2.00 1.300 1.900 -0.657692 + 4P 2 1 1.00 2.000 2.200 -0.189559 + 3D 3 2 10.00 1.300 1.900 -1.407825 + Generation configuration: + 4S 1 0 2.00 1.300 1.900 -0.657690 + 4S 1 0 0.00 1.300 1.900 6.000000 + 4P 2 1 1.00 2.000 2.200 -0.189559 + 4P 2 1 0.00 2.000 2.200 6.000000 + 3D 3 2 10.00 1.300 1.900 -1.407824 + 3D 3 2 0.00 1.300 1.900 0.050000 + + Pseudization used: troullier-martins + + &input + title='Ga', + zed=31., + rel=1, + config='[Ar] 4s2 4p1 3d10', + iswitch=3, + dft='PBE' + / + &inputp + lpaw=.true., + pseudotype=3, + file_pseudopw='Ga.pbe-dn-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=1.9, + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., + 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ntyp = 2 + ecutwfc = 60 + ecutrho = 244 +/ + +&electrons + mixing_mode = 'plain' + mixing_beta = 0.7 + conv_thr = 1.0e-13 +/ + +ATOMIC_SPECIES + Ga 69.723 Ga.pbe-dn-kjpaw_psl.1.0.0.UPF + As 74.921595 As.pbe-n-kjpaw_psl.1.0.0.UPF + +ATOMIC_POSITIONS + Ga 0.00 0.00 0.00 + As 0.25 0.25 0.25 + +K_POINTS {automatic} + 8 8 8 0 0 0 diff --git a/Ladygin/examples/inputs/NaCl/pseudo/Cl.pbe-n-kjpaw_psl.0.1.UPF b/Ladygin/examples/inputs/NaCl/pseudo/Cl.pbe-n-kjpaw_psl.0.1.UPF new file mode 100644 index 00000000..ed33966a --- /dev/null +++ b/Ladygin/examples/inputs/NaCl/pseudo/Cl.pbe-n-kjpaw_psl.0.1.UPF @@ -0,0 +1,10063 @@ + + +Generated using "atomic" code by A. Dal Corso v.5.0.2 svn rev. 9415 +Author: ADC +Generation date: 11Sep2012 +Pseudopotential type: PAW +Element: Cl +Functional: SLA PW PBX PBC + +Suggested minimum cutoff for wavefunctions: 36. Ry +Suggested minimum cutoff for charge density: 146. Ry +The Pseudo was generated with a Scalar-Relativistic Calculation +L component and cutoff radius for Local Potential: 2 1.8000 + +Valence configuration: +nl pn l occ Rcut Rcut US E pseu +3S 1 0 2.00 1.600 1.800 -1.520834 +3P 2 1 5.00 1.900 2.100 -0.631739 +Generation configuration: +3S 1 0 2.00 1.600 1.800 -1.520831 +3S 1 0 0.00 1.000 1.800 6.200000 +3P 2 1 5.00 1.900 2.100 -0.631737 +3P 2 1 0.00 1.000 2.100 6.200000 +3D 3 2 -2.00 1.800 1.800 0.150000 + +Pseudization used: troullier-martins + +title='Cl', +zed=17, +rel=1, +config='[Ne] 3s2 3p5 3d-2', +iswitch=3, +dft='PBE' +/ +&inputp +lpaw=.true., +pseudotype=3, +file_pseudopw='Cl.pbe-n-kjpaw_psl.0.1.UPF', +author='ADC', +lloc=2, +which_augfun ='PSQ', +rmatch_augfun= 1.7, +nlcc=.true., +new_core_ps = .true., +rcore=1.0, +tm=.true. +/ +5 +3S 1 0 2.00 0.00 1.60 1.80 0.0 +3S 1 0 0.00 6.20 1.00 1.80 0.0 +3P 2 1 5.00 0.00 1.90 2.10 0.0 +3P 2 1 0.00 6.20 1.00 2.10 0.0 +3D 3 2 -2.00 0.15 1.80 1.80 0.0 + + + + + + + + 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b/Ladygin/examples/inputs/NaCl/pseudo/Na.pbe-spn-kjpaw_psl.0.2.UPF @@ -0,0 +1,10041 @@ + + +Generated using "atomic" code by A. Dal Corso v.5.0.2 svn rev. 9415 +Author: ADC +Generation date: 11Sep2012 +Pseudopotential type: PAW +Element: Na +Functional: SLA PW PBX PBC + +Suggested minimum cutoff for wavefunctions: 37. Ry +Suggested minimum cutoff for charge density: 147. Ry +The Pseudo was generated with a Scalar-Relativistic Calculation +L component and cutoff radius for Local Potential: 2 2.0000 + +Valence configuration: +nl pn l occ Rcut Rcut US E pseu +2S 1 0 2.00 1.500 1.800 -4.430681 +3S 2 0 0.50 1.500 1.800 -0.376698 +2P 2 1 6.00 1.500 2.000 -2.366307 +3P 3 1 0.00 1.500 2.000 -0.192082 +Generation configuration: +2S 1 0 2.00 1.500 1.800 -4.430690 +3S 2 0 0.50 1.500 1.800 -0.376700 +2P 2 1 6.00 1.500 2.000 -2.366316 +3P 3 1 0.00 1.500 2.000 -0.192083 +3D 3 2 -2.00 2.000 2.000 0.300000 + +Pseudization used: troullier-martins + +title='Na', +zed=11.0, +rel=1, +config='[Ne] 3s0.5 3p0 3d-2', +iswitch=3, +dft='PBE' +/ +&inputp +lpaw=.true., +pseudotype=3, +file_pseudopw='Na.pbe-spn-kjpaw_psl.0.2.UPF', +author='ADC', +lloc=2, +which_augfun='PSQ', +rmatch_augfun=1.6, +tm=.true. +/ +5 +2S 1 0 2.00 0.00 1.50 1.80 0.0 +3S 2 0 0.50 0.00 1.50 1.80 0.0 +2P 2 1 6.00 0.00 1.50 2.00 0.0 +3P 3 1 0.00 0.00 1.50 2.00 0.0 +3D 3 2 -2.00 0.30 2.00 2.00 0.0 + + + + + + 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2.8451507380878356 2.8451507380878356 +2.8451507380878356 0 2.8451507380878356 +2.8451507380878356 2.8451507380878356 0 +K_POINTS automatic +8 8 8 0 0 0 diff --git a/Ladygin/examples/inputs/Ni/pseudo/ni_pbe_v1.4.uspp.F.UPF b/Ladygin/examples/inputs/Ni/pseudo/ni_pbe_v1.4.uspp.F.UPF new file mode 100644 index 00000000..f69c05e1 --- /dev/null +++ b/Ladygin/examples/inputs/Ni/pseudo/ni_pbe_v1.4.uspp.F.UPF @@ -0,0 +1,8115 @@ + +Generated using Vanderbilt code, version 7 3 6 +Author: kfg Generation date: 1 4 15 +Automatically converted from original format + 1 The Pseudo was generated with a Scalar-Relativistic Calculation + 1.50000000000E+00 Local Potential cutoff radius +nl pn l occ Rcut Rcut US E pseu +3S 3 0 2.00 0.00000000000 1.44000000000 -9.60941084200 +3P 3 1 6.00 0.00000000000 1.55000000000 -6.69550237500 +3D 3 2 8.00 0.00000000000 1.50000000000 -2.08482978100 +4S 4 0 0.00 0.00000000000 1.44000000000 -1.59107856400 +4P 4 1 0.00 0.00000000000 1.55000000000 -1.10274995100 + + + + + 0 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6.55567849021E-04 6.89746719837E-04 + 7.25701537270E-04 7.63524132182E-04 8.03311054506E-04 8.45163826522E-04 + 8.89189144306E-04 9.35499206309E-04 9.84211909309E-04 1.03545122822E-03 + 1.08934744479E-03 1.14603750780E-03 1.20566536256E-03 1.26838227978E-03 + 1.33434724667E-03 1.40372735895E-03 1.47669819141E-03 1.55344425153E-03 + 1.63415943001E-03 1.71904740871E-03 1.80832225631E-03 1.90220881652E-03 + 2.00094334160E-03 2.10477398481E-03 2.21396145233E-03 2.32877955765E-03 + 2.44951589187E-03 2.57647252840E-03 2.70996665917E-03 2.85033139441E-03 + 2.99791652098E-03 3.15308926170E-03 3.31623519824E-03 3.48775910416E-03 + 3.66808584103E-03 3.85766140980E-03 4.05695384699E-03 4.26645437253E-03 + 4.48667839208E-03 4.71816675807E-03 4.96148681311E-03 5.21723376738E-03 + 5.48603191482E-03 5.76853601379E-03 6.06543267874E-03 6.37744186280E-03 + 6.70531833223E-03 7.04985332297E-03 7.41187613633E-03 7.79225583079E-03 + 8.19190307753E-03 8.61177186961E-03 9.05286161859E-03 9.51621892612E-03 + 1.00029398065E-02 1.05141717240E-02 1.10511157583E-02 1.16150290268E-02 + 1.22072267803E-02 1.28290850680E-02 1.34820430762E-02 1.41676057260E-02 + 1.48873463235E-02 1.56429092544E-02 1.64360127591E-02 1.72684518073E-02 + 1.81421009303E-02 1.90589172909E-02 2.00209436307E-02 2.10303114015E-02 + 2.20892438511E-02 2.32000592334E-02 2.43651739848E-02 2.55871059704E-02 + 2.68684776999E-02 2.82120195892E-02 2.96205732417E-02 3.10970945777E-02 + 3.26446570823E-02 3.42664549353E-02 3.59658060164E-02 3.77461549070E-02 + 3.96110756805E-02 4.15642746698E-02 4.36095929271E-02 4.57510085597E-02 + 4.79926388287E-02 5.03387418824E-02 5.27937183985E-02 5.53621125332E-02 + 5.80486127439E-02 6.08580520286E-02 6.37954076634E-02 6.68658003988E-02 + 7.00744928949E-02 7.34268876862E-02 7.69285240543E-02 8.05850743170E-02 + 8.44023389003E-02 8.83862406898E-02 9.25428179797E-02 9.68782159853E-02 + 1.01398678002E-01 1.06110533533E-01 1.11020186332E-01 1.16134099830E-01 + 1.21458781086E-01 1.27000762736E-01 1.32766582654E-01 1.38762761825E-01 + 1.44995779348E-01 1.51472045032E-01 1.58197869698E-01 1.65179432144E-01 + 1.72422743467E-01 1.79933608195E-01 1.87717582367E-01 1.95779927726E-01 + 2.04125563640E-01 2.12759013714E-01 2.21684350753E-01 2.30905136424E-01 + 2.40424358350E-01 2.50244362854E-01 2.60366784498E-01 2.70792471974E-01 + 2.81521410576E-01 2.92552641462E-01 3.03884178722E-01 3.15512922338E-01 + 3.27434571678E-01 3.39643534287E-01 3.52132836964E-01 3.64894034132E-01 + 3.77917119285E-01 3.91190435860E-01 4.04700593975E-01 4.18432388455E-01 + 4.32368726020E-01 4.46490556447E-01 4.60776817129E-01 4.75204385096E-01 + 4.89748046582E-01 5.04380479099E-01 5.19072255580E-01 5.33791866536E-01 + 5.48505770073E-01 5.63178464781E-01 5.77772596913E-01 5.92249097418E-01 + 6.06567355334E-01 6.20685431605E-01 6.34560309193E-01 6.48148191228E-01 + 6.61404836751E-01 6.74285946349E-01 6.86747585936E-01 6.98746657075E-01 + 7.10241400647E-01 7.21191938406E-01 7.31560834999E-01 7.41313680592E-01 + 7.50419671009E-01 7.58852179370E-01 7.66589288679E-01 7.73614272538E-01 + 7.79915984120E-01 7.85489129958E-01 7.90334384937E-01 7.94458305044E-01 + 7.97872996754E-01 8.00595480771E-01 8.02646706880E-01 8.04050150275E-01 + 8.04829943192E-01 8.05008475541E-01 8.04603422841E-01 8.03624152633E-01 + 8.02067490966E-01 7.99912832771E-01 7.97116631698E-01 7.93606314865E-01 + 7.89273744393E-01 7.83968377904E-01 7.77490377945E-01 7.69583978738E-01 + 7.59931538044E-01 7.48148791206E-01 7.33781950120E-01 7.16307412966E-01 + 6.95134954801E-01 6.69615395377E-01 6.39053792826E-01 6.02729271117E-01 + 5.59922524423E-01 5.09951929586E-01 4.52218896040E-01 3.86262656153E-01 + 3.11823989249E-01 2.28916442344E-01 1.37902306712E-01 3.95689907471E-02 + -6.48005993923E-02 -1.73372404911E-01 -2.83731654345E-01 -3.92893449821E-01 + -4.97370028220E-01 -5.93312926723E-01 -6.76748521464E-01 -7.43923090645E-01 + -7.91767261285E-01 -8.18477681975E-01 -8.24193747280E-01 -8.11716930141E-01 + -7.87177445461E-01 -7.60496341088E-01 -7.45421640284E-01 -7.58840568789E-01 + -8.19000434198E-01 -9.42238036474E-01 -1.13787640266E+00 -1.40119517771E+00 + -1.70498064980E+00 -1.99138194673E+00 -2.16808008345E+00 -2.11674275979E+00 + -1.72902522258E+00 -9.92161928260E-01 -2.07810438452E-01 1.38265224889E-02 + 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 + 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 + 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 + 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 + 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 + 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 + 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 + 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 + 0.00000000000E+00 0.00000000000E+00 0.00000000000E+00 + + + 9 Number of nonzero Dij + 1 1 -1.30865721120E+00 + 1 2 -8.88137555576E-01 + 2 2 3.99081739341E+00 + 3 3 1.53810539424E+01 + 3 4 -2.58263095426E+01 + 4 4 4.04846536745E+01 + 5 5 6.59071259279E+01 + 5 6 6.82458386013E+01 + 6 6 7.04904877454E+01 + + + 10 nqf. If not zero, Qij's inside rinner are computed using qfcoef's + + 1 9.80000000000E-01 + 2 9.80000000000E-01 + 3 9.80000000000E-01 + 4 9.80000000000E-01 + 5 9.80000000000E-01 + + 1 1 0 i j (l(j)) + 1.19798887044E-01 Q_int + 0.00000000000E+00 -1.48582935763E-11 -6.04534429343E-11 -1.38361882921E-10 + -2.50223234254E-10 -3.97742633085E-10 -5.82693143477E-10 -8.06918255679E-10 + -1.07233447321E-09 -1.38093399186E-09 -1.73478747382E-09 -2.13604692029E-09 + -2.58694864593E-09 -3.08981635888E-09 -3.64706434981E-09 -4.26120079402E-09 + -4.93483117041E-09 -5.67066180146E-09 -6.47150351843E-09 -7.34027545613E-09 + -8.28000898181E-09 -9.29385176288E-09 -1.03850719782E-08 -1.15570626780E-08 + -1.28133462980E-08 -1.41575793317E-08 -1.55935571689E-08 -1.71252191036E-08 + -1.87566535183E-08 -2.04921032519E-08 -2.23359711554E-08 -2.42928258435E-08 + -2.63674076489E-08 -2.85646347856E-08 -3.08896097286E-08 -3.33476258185E-08 + -3.59441740976E-08 -3.86849503858E-08 -4.15758626061E-08 -4.46230383654E-08 + 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a/Ladygin/examples/inputs/Ni/pw.in b/Ladygin/examples/inputs/Ni/pw.in new file mode 100644 index 00000000..2152f2ec --- /dev/null +++ b/Ladygin/examples/inputs/Ni/pw.in @@ -0,0 +1,37 @@ + &control + calculation='scf' + restart_mode='from_scratch' + pseudo_dir = '../pseudos/' + outdir='./tmp/' + prefix='ni' + / + + &system + ibrav=2 + celldm(1) =6.648 + nat=1 + ntyp=1 + nspin = 2 + starting_magnetization(1)=0.7 + nbnd = 10 + ecutwfc = 45.0 + ecutrho = 360.0 + occupations='smearing' + smearing='m-v' + degauss=0.01 + / + + &electrons + diagonalization='david' + conv_thr = 1.0e-13 + mixing_beta = 0.7 + / + +ATOMIC_SPECIES + Ni 58.69 ni_pbe_v1.4.uspp.F.UPF + +ATOMIC_POSITIONS alat + Ni 0.0 0.0 0.0 + +K_POINTS AUTOMATIC +14 14 14 1 1 1 diff --git a/Ladygin/examples/inputs/Pb/pseudo/Pb.pbe-dn-kjpaw_psl.1.0.0.UPF b/Ladygin/examples/inputs/Pb/pseudo/Pb.pbe-dn-kjpaw_psl.1.0.0.UPF new file mode 100644 index 00000000..d3f7c329 --- /dev/null +++ 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+1,32 @@ +&CONTROL + calculation= 'scf' + restart_mode= 'from_scratch' + prefix= 'pb' + outdir= '/tmp/' + pseudo_dir= '../pseudos/' +/ + +&SYSTEM + ibrav= 2 + celldm(1) = 9.354144 + nat= 1 + ntyp= 1 + ecutwfc= 50 + ecutrho= 500 + occupations= 'smearing' + smearing= 'gaussian' + degauss= 0.01 +/ + +&ELECTRONS + conv_thr= 1e-13 +/ + +ATOMIC_SPECIES + Pb 207.2 Pb.pbe-dn-kjpaw_psl.1.0.0.UPF + +ATOMIC_POSITIONS (alat) + Pb 0.00 0.00 0.00 + +K_POINTS (automatic) + 10 10 10 0 0 0 diff --git a/Ladygin/examples/inputs/SiO2/SiO2_mp-8352_primitive.cif b/Ladygin/examples/inputs/SiO2/SiO2_mp-8352_primitive.cif new file mode 100644 index 00000000..af81d942 --- /dev/null +++ b/Ladygin/examples/inputs/SiO2/SiO2_mp-8352_primitive.cif @@ -0,0 +1,32 @@ +# generated using pymatgen +data_SiO2 +_symmetry_space_group_name_H-M 'P 1' +_cell_length_a 5.27563036 +_cell_length_b 5.27563036 +_cell_length_c 5.27563036 +_cell_angle_alpha 60.00000000 +_cell_angle_beta 60.00000000 +_cell_angle_gamma 60.00000000 +_symmetry_Int_Tables_number 1 +_chemical_formula_structural SiO2 +_chemical_formula_sum 'Si2 O4' +_cell_volume 103.82646741 +_cell_formula_units_Z 2 +loop_ + _symmetry_equiv_pos_site_id + _symmetry_equiv_pos_as_xyz + 1 'x, y, z' +loop_ + _atom_site_type_symbol + _atom_site_label + _atom_site_symmetry_multiplicity + _atom_site_fract_x + _atom_site_fract_y + _atom_site_fract_z + _atom_site_occupancy + Si Si0 1 0.25000000 0.25000000 0.25000000 1 + Si Si1 1 0.00000000 0.00000000 1.00000000 1 + O O2 1 0.12500000 0.12500000 0.62500000 1 + O O3 1 0.12500000 0.62500000 0.12500000 1 + O O4 1 0.12500000 0.12500000 0.12500000 1 + O O5 1 0.62500000 0.12500000 0.12500000 1 diff --git a/Ladygin/examples/inputs/SiO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF b/Ladygin/examples/inputs/SiO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF new file mode 100644 index 00000000..c463e348 --- /dev/null +++ b/Ladygin/examples/inputs/SiO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF @@ -0,0 +1,9766 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.3 +Author: ADC +Generation date: 6Sep2018 +Pseudopotential type: PAW +Element: O +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 47. Ry + Suggested minimum cutoff for charge density: 323. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.1000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 2S 1 0 2.00 1.000 1.300 -1.761150 + 2P 2 1 4.00 0.900 1.350 -0.663754 + Generation configuration: + 2S 1 0 2.00 1.000 1.300 -1.761144 + 2S 1 0 0.00 1.000 1.300 1.000000 + 2P 2 1 4.00 0.900 1.350 -0.663748 + 2P 2 1 0.00 0.900 1.350 0.050000 + Pseudization used: troullier-martins + + + &input + title='O', + zed=8., + rel=1, + config='[He] 2s2 2p4', + iswitch=3, + dft='PBE' + / + &inputp + lpaw = .true., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='O.pbe-n-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=1.1 + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., + new_core_ps=.true., + rcore=0.7, + tm=.true. + / +4 +2S 1 0 2.00 0.00 1.00 1.30 0.0 +2S 1 0 0.00 1.00 1.00 1.30 0.0 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Ladygin/test_case/pseudo/Si-PBE.upf rename to Ladygin/examples/inputs/SiO2/pseudo/Si-PBE.upf diff --git a/Ladygin/examples/inputs/SiO2/pw.in b/Ladygin/examples/inputs/SiO2/pw.in new file mode 100644 index 00000000..bef3dd5a --- /dev/null +++ b/Ladygin/examples/inputs/SiO2/pw.in @@ -0,0 +1,43 @@ +&control + calculation = 'scf' + prefix = 'sio2' + outdir = '/tmp/' + pseudo_dir = '../pseudos/' + verbosity = 'high' + wf_collect = .true. +/ + +&system + ibrav = 0 + nat = 6 + ntyp = 2 + ecutwfc = 60 + ecutrho = 244 +/ + +&electrons + mixing_mode = 'plain' + mixing_beta = 0.7 + conv_thr = 1.0e-13 +/ + +ATOMIC_SPECIES + Si 28.0855 Si-PBE.upf + O 15.999 O.pbe-n-kjpaw_psl.1.0.0.UPF + +ATOMIC_POSITIONS crystal +Si 0.25000000000000 0.25000000000000 0.25000000000000 +Si 0.00000000000000 0.00000000000000 0.00000000000000 + O 0.12500000000000 0.12500000000000 0.62500000000000 + O 0.12500000000000 0.62500000000000 0.12500000000000 + O 0.12500000000000 0.12500000000000 0.12500000000000 + O 0.62500000000000 0.12500000000000 0.12500000000000 + +K_POINTS automatic +3 3 3 0 0 0 + +CELL_PARAMETERS {bohr} + 9.96949655873689 0.00000000000000 0.00000000000000 + 4.98474827936845 8.63383728280769 0.00000000000000 + 4.98474827936845 2.87794576093590 8.14005985377940 + diff --git a/Ladygin/examples/inputs/ZnO/pseudo/.ipynb_checkpoints/O.pw-mt_fhi-checkpoint.UPF b/Ladygin/examples/inputs/ZnO/pseudo/.ipynb_checkpoints/O.pw-mt_fhi-checkpoint.UPF new file mode 100644 index 00000000..dbb8f987 --- /dev/null +++ b/Ladygin/examples/inputs/ZnO/pseudo/.ipynb_checkpoints/O.pw-mt_fhi-checkpoint.UPF @@ -0,0 +1,1770 @@ + + +Generated using FHI98PP, converted with fhi2upf.x v.5.0.1 +Author: Abinit web site +Generation date: 10-Jul-2012, PG +Pseudopotential type: SL +Element: O +Functional: SLA-PW + +Suggested minimum cutoff for wavefunctions: 0. Ry +Suggested minimum cutoff for charge density: 0. Ry +The Pseudo was generated with a Scalar-Relativistic Calculation +L component and cutoff radius for Local Potential: 2 0.0000 + +Valence configuration: +nl pn l occ Rcut Rcut US E pseu +2s 2 0 2.00 0.000 0.000 0.000000 +2p 2 1 4.00 0.000 0.000 0.000000 +3d 3 2 0.00 0.000 0.000 0.000000 +4f 4 3 0.00 0.000 0.000 0.000000 + +Generation configuration: not available. +Comment: +oxygen, fhi98PP : Trouiller-Martins-type, LDA Ceperley/Alder Perdew/Wang (1992), + + + + + + + +7.812500000000000E-004 8.005468750000000E-004 8.203203828125000E-004 8.405822962679700E-004 +8.613446789857900E-004 8.826198925567400E-004 9.044206039028900E-004 9.267597928192900E-004 +9.496507597019300E-004 9.731071334665600E-004 9.971428796631901E-004 1.021772308790900E-003 +1.047010084818000E-003 1.072871233913000E-003 1.099371153390700E-003 1.126525620879400E-003 +1.154350803715100E-003 1.182863268566900E-003 1.212079991300500E-003 1.242018367085600E-003 +1.272696220752600E-003 1.304131817405200E-003 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0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 + + diff --git a/Ladygin/examples/inputs/ZnO/pseudo/O.pw-mt_fhi.UPF b/Ladygin/examples/inputs/ZnO/pseudo/O.pw-mt_fhi.UPF new file mode 100644 index 00000000..dbb8f987 --- /dev/null +++ b/Ladygin/examples/inputs/ZnO/pseudo/O.pw-mt_fhi.UPF @@ -0,0 +1,1770 @@ + + +Generated using FHI98PP, converted with fhi2upf.x v.5.0.1 +Author: Abinit web site +Generation date: 10-Jul-2012, PG +Pseudopotential type: SL +Element: O +Functional: SLA-PW + +Suggested minimum cutoff for wavefunctions: 0. Ry +Suggested minimum cutoff for charge density: 0. Ry +The Pseudo was generated with a Scalar-Relativistic Calculation +L component and cutoff radius for Local Potential: 2 0.0000 + +Valence configuration: +nl pn l occ Rcut Rcut US E pseu +2s 2 0 2.00 0.000 0.000 0.000000 +2p 2 1 4.00 0.000 0.000 0.000000 +3d 3 2 0.00 0.000 0.000 0.000000 +4f 4 3 0.00 0.000 0.000 0.000000 + +Generation configuration: not available. +Comment: +oxygen, fhi98PP : Trouiller-Martins-type, LDA Ceperley/Alder Perdew/Wang (1992), + + + + + + + +7.812500000000000E-004 8.005468750000000E-004 8.203203828125000E-004 8.405822962679700E-004 +8.613446789857900E-004 8.826198925567400E-004 9.044206039028900E-004 9.267597928192900E-004 +9.496507597019300E-004 9.731071334665600E-004 9.971428796631901E-004 1.021772308790900E-003 +1.047010084818000E-003 1.072871233913000E-003 1.099371153390700E-003 1.126525620879400E-003 +1.154350803715100E-003 1.182863268566900E-003 1.212079991300500E-003 1.242018367085600E-003 +1.272696220752600E-003 1.304131817405200E-003 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0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 + + diff --git a/Ladygin/examples/inputs/ZnO/pseudo/Zn.pw-mt_fhi.UPF b/Ladygin/examples/inputs/ZnO/pseudo/Zn.pw-mt_fhi.UPF new file mode 100644 index 00000000..b8a9d560 --- /dev/null +++ b/Ladygin/examples/inputs/ZnO/pseudo/Zn.pw-mt_fhi.UPF @@ -0,0 +1,1952 @@ + + +Generated using FHI98PP, converted with fhi2upf.x v.5.0.1 +Author: Abinit web site +Generation date: 10-Jul-2012, PG +Pseudopotential type: SL +Element: Zn +Functional: SLA-PW + +Suggested minimum cutoff for wavefunctions: 0. Ry +Suggested minimum cutoff for charge density: 0. Ry +The Pseudo was generated with a Non-Relativistic Calculation +L component and cutoff radius for Local Potential: 0 0.0000 + +Valence configuration: +nl pn l occ Rcut Rcut US E pseu +4s 4 0 2.00 0.000 0.000 0.000000 +4p 4 1 0.00 0.000 0.000 0.000000 +3d 3 2 10.00 0.000 0.000 0.000000 +4f 4 3 0.00 0.000 0.000 0.000000 + +Generation configuration: not available. +Comment: +zinc, fhi98PP : Trouiller-Martins-type, LDA Ceperley/Alder Perdew/Wang (1992), l + + + + + + + +2.083333333333300E-004 2.134791666666700E-004 2.187521020833300E-004 2.241552790047900E-004 +2.296919143962100E-004 2.353653046818000E-004 2.411788277074400E-004 2.471359447518100E-004 +2.532402025871800E-004 2.594952355910800E-004 2.659047679101800E-004 2.724726156775600E-004 +2.792026892848000E-004 2.860989957101400E-004 2.931656409041800E-004 3.004068322345100E-004 +3.078268809907000E-004 3.154302049511700E-004 3.232213310134600E-004 3.312048978895000E-004 +3.393856588673700E-004 3.477684846413900E-004 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0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 +0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 + + diff --git a/Ladygin/examples/inputs/ZnO/pw.in b/Ladygin/examples/inputs/ZnO/pw.in new file mode 100644 index 00000000..1faa1753 --- /dev/null +++ b/Ladygin/examples/inputs/ZnO/pw.in @@ -0,0 +1,36 @@ +&CONTROL + calculation = "scf" + prefix = "ZNO" + pseudo_dir = "/home/larrucea/Software/MM/espresso-5.0.2/pseudo" + outdir = "/home/larrucea/tmp" +/ +&SYSTEM + ibrav=0 + celldm(1) =6.330582528 + nat=4 + ntyp= 2 + occupations='smearing' + smearing='gauss' + degauss=0.02 + ecutwfc =80.0 +/ +&ELECTRONS + mixing_mode='plain' + mixing_beta = 0.5 + startingwfc='random' + conv_thr = 1.0d-13 +/ +CELL_PARAMETERS alat + 1.55820896 0.00000000 0.00000000 + 0.00000000 0.86602540 -0.50000000 + 0.00000000 0.00000000 1.00000000 +ATOMIC_SPECIES + Zn 65.409 Zn.pw-mt_fhi.UPF + O 15.999 O.pw-mt_fhi.UPF +ATOMIC_POSITIONS +Zn 2.010975287 0.487933254 -0.051360548 +Zn 1.234717421 0.199473387 0.448322227 +O 1.051679030 0.488287222 -0.051814333 +O 1.830251369 0.199830262 0.448810714 +K_POINTS (automatic) +2 2 2 0 0 0 diff --git a/Ladygin/examples/inputs/ZrO2/.ipynb_checkpoints/pw-checkpoint.in b/Ladygin/examples/inputs/ZrO2/.ipynb_checkpoints/pw-checkpoint.in new file mode 100644 index 00000000..fcacd8ca --- /dev/null +++ b/Ladygin/examples/inputs/ZrO2/.ipynb_checkpoints/pw-checkpoint.in @@ -0,0 +1,40 @@ +&control + calculation = 'scf' + prefix = 'ZrO2' + outdir = '/tmp/' + pseudo_dir = '../pseudos/' + verbosity = 'high' + wf_collect = .true. +/ + +&system + ibrav = 0 + nat = 3 + ntyp = 2 + ecutwfc = 60 + ecutrho = 244 +/ + +&electrons + mixing_mode = 'plain' + mixing_beta = 0.7 + conv_thr = 1.0e-13 +/ + +ATOMIC_SPECIES + Zr 91.224 Zr.pbesol-spn-kjpaw_psl.1.0.0.UPF + O 15.999 O.pbe-n-kjpaw_psl.1.0.0.UPF + +CELL_PARAMETERS angstrom +2.574983 2.574983 0.000000 +0.000000 2.574983 2.574983 +2.574983 0.000000 2.574983 + +ATOMIC_POSITIONS +Zr 0.000000 0.000000 0.000000 +O 0.250000 0.250000 0.250000 +O 0.750000 0.750000 0.750000 + + +K_POINTS {automatic} + 8 8 8 0 0 0 diff --git a/Ladygin/examples/inputs/ZrO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF b/Ladygin/examples/inputs/ZrO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF new file mode 100644 index 00000000..c463e348 --- /dev/null +++ b/Ladygin/examples/inputs/ZrO2/pseudo/O.pbe-n-kjpaw_psl.1.0.0.UPF @@ -0,0 +1,9766 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.3 +Author: ADC +Generation date: 6Sep2018 +Pseudopotential type: PAW +Element: O +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 47. Ry + Suggested minimum cutoff for charge density: 323. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.1000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 2S 1 0 2.00 1.000 1.300 -1.761150 + 2P 2 1 4.00 0.900 1.350 -0.663754 + Generation configuration: + 2S 1 0 2.00 1.000 1.300 -1.761144 + 2S 1 0 0.00 1.000 1.300 1.000000 + 2P 2 1 4.00 0.900 1.350 -0.663748 + 2P 2 1 0.00 0.900 1.350 0.050000 + Pseudization used: troullier-martins + + + &input + title='O', + zed=8., + rel=1, + config='[He] 2s2 2p4', + iswitch=3, + dft='PBE' + / + &inputp + lpaw = .true., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='O.pbe-n-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=1.1 + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., + new_core_ps=.true., + rcore=0.7, + tm=.true. + / +4 +2S 1 0 2.00 0.00 1.00 1.30 0.0 +2S 1 0 0.00 1.00 1.00 1.30 0.0 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file mode 100644 index 00000000..c9713944 --- /dev/null +++ b/Ladygin/examples/inputs/ZrO2/pseudo/Zr.pbe-spn-kjpaw_psl.1.0.0.UPF @@ -0,0 +1,21787 @@ + + +Generated using "atomic" code by A. Dal Corso v.6.2.2 +Author: ADC +Generation date: 2May2018 +Pseudopotential type: PAW +Element: Zr +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 46. Ry + Suggested minimum cutoff for charge density: 267. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.5000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 4S 1 0 2.00 1.200 1.300 -4.005980 + 5S 2 0 2.00 1.200 1.300 -0.326268 + 4P 2 1 6.00 1.200 1.500 -2.385850 + 5P 3 1 0.00 1.200 1.500 -0.107987 + 4D 3 2 2.00 1.200 1.600 -0.264499 + Generation configuration: + 4S 1 0 2.00 1.200 1.300 -4.006009 + 5S 2 0 2.00 1.200 1.300 -0.326276 + 4P 2 1 6.00 1.200 1.500 -2.385870 + 5P 3 1 0.00 1.200 1.500 -0.107990 + 4D 3 2 2.00 1.200 1.600 -0.264515 + 4D 3 2 0.00 1.200 1.600 0.500000 + Pseudization used: troullier-martins + + + &input + title='Zr', + zed=40., + rel=1, + config='[Kr] 5s2 5p0 4d2', + iswitch=3, + dft='PBE' + / + &inputp + lpaw=.true., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='Zr.pbe-spn-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=1.5, + which_augfun='PSQ', + rmatch_augfun_nc=.true., + nlcc=.true., + new_core_ps=.true., + rcore=1.2, + tm=.true. + / +6 +4S 1 0 2.00 0.00 1.20 1.30 0.0 +5S 2 0 2.00 0.00 1.20 1.30 0.0 +4P 2 1 6.00 0.00 1.20 1.50 0.0 +5P 3 1 0.00 0.00 1.20 1.50 0.0 +4D 3 2 2.00 0.00 1.20 1.60 0.0 +4D 3 2 0.00 0.50 1.20 1.60 0.0 + + + + + + + 2.279704913886290e-5 2.308380071672556e-5 2.337415918541455e-5 2.366816991403135e-5 + 2.396587884235049e-5 2.426733248799772e-5 2.457257795371846e-5 2.488166293473778e-5 + 2.519463572621277e-5 2.551154523077886e-5 2.583244096619093e-5 2.615737307306047e-5 + 2.648639232269026e-5 2.681955012500742e-5 2.715689853659645e-5 2.749849026883295e-5 + 2.784437869612008e-5 2.819461786422830e-5 2.854926249874018e-5 2.890836801360144e-5 + 2.927199051977936e-5 2.964018683403032e-5 3.001301448777753e-5 3.039053173610037e-5 + 3.077279756683702e-5 3.115987170980123e-5 3.155181464611536e-5 3.194868761766061e-5 + 3.235055263664620e-5 3.275747249529900e-5 3.316951077567479e-5 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0.250000 +O 0.750000 0.750000 0.750000 + + +K_POINTS {automatic} + 8 8 8 0 0 0 diff --git a/Ladygin/examples/run.sh b/Ladygin/examples/run.sh index 7d70008d..416b84fb 100755 --- a/Ladygin/examples/run.sh +++ b/Ladygin/examples/run.sh @@ -20,8 +20,9 @@ module swap gpu cpu curr_path=$( pwd ) -for mat in CsPbBr3; do +for mat in Al Si MbB2 NaCl Ni GaAs Cu2O ZrO2 SiO2 CsPdBr3 Pb; do + rm -r calcs/${mat} cp -r inputs/${mat} calcs - ConvTrack -workdir "${curr_path}/calcs/${mat}" -mode "qe" -target "total_energy" -eps 0.01 -input "pw.in" -encut 40 -calc "par" -ncores 8 -nk 8 -k_range 2 80 4 + ConvTrack -workdir "${curr_path}/calcs/${mat}" -mode "qe" -target "total_energy" -eps 0.01 -input "pw.in" -encut 40 -calc "par" -ncores 32 -nk 8 -k_range 2 80 4 done diff --git a/Ladygin/test_case/pw.out b/Ladygin/test_case/pw.out deleted file mode 100644 index fa342316..00000000 --- a/Ladygin/test_case/pw.out +++ /dev/null @@ -1,280 +0,0 @@ - - Program PWSCF v.7.3.1 starts on 17Apr2025 at 23: 8:23 - - This program is part of the open-source Quantum ESPRESSO suite - for quantum simulation of materials; please cite - "P. Giannozzi et al., J. Phys.:Condens. Matter 21 395502 (2009); - "P. Giannozzi et al., J. Phys.:Condens. Matter 29 465901 (2017); - "P. Giannozzi et al., J. Chem. Phys. 152 154105 (2020); - URL http://www.quantum-espresso.org", - in publications or presentations arising from this work. More details at - http://www.quantum-espresso.org/quote - - Parallel version (MPI & OpenMP), running on 4 processor cores - Number of MPI processes: 4 - Threads/MPI process: 1 - - MPI processes distributed on 1 nodes - 490329 MiB available memory on the printing compute node when the environment starts - - Reading input from pw.in - - Current dimensions of program PWSCF are: - Max number of different atomic species (ntypx) = 10 - Max number of k-points (npk) = 40000 - Max angular momentum in pseudopotentials (lmaxx) = 4 - - K-points division: npool = 4 - Subspace diagonalization in iterative solution of the eigenvalue problem: - a serial algorithm will be used - - - G-vector sticks info - -------------------- - sticks: dense smooth PW G-vecs: dense smooth PW - Sum 583 583 187 9257 9257 1687 - - Using Slab Decomposition - - - - bravais-lattice index = 2 - lattice parameter (alat) = 10.2620 a.u. - unit-cell volume = 270.1693 (a.u.)^3 - number of atoms/cell = 2 - number of atomic types = 1 - number of electrons = 8.00 - number of Kohn-Sham states= 8 - kinetic-energy cutoff = 40.0000 Ry - charge density cutoff = 160.0000 Ry - scf convergence threshold = 1.0E-13 - mixing beta = 0.7000 - number of iterations used = 8 plain mixing - Exchange-correlation= PBE - ( 1 4 3 4 0 0 0) - - celldm(1)= 10.262000 celldm(2)= 0.000000 celldm(3)= 0.000000 - celldm(4)= 0.000000 celldm(5)= 0.000000 celldm(6)= 0.000000 - - crystal axes: (cart. coord. in units of alat) - a(1) = ( -0.500000 0.000000 0.500000 ) - a(2) = ( 0.000000 0.500000 0.500000 ) - a(3) = ( -0.500000 0.500000 0.000000 ) - - reciprocal axes: (cart. coord. in units 2 pi/alat) - b(1) = ( -1.000000 -1.000000 1.000000 ) - b(2) = ( 1.000000 1.000000 1.000000 ) - b(3) = ( -1.000000 1.000000 -1.000000 ) - - - PseudoPot. # 1 for Si read from file: - ./Si-PBE.upf - MD5 check sum: d8e8ea3dd55db7858206a05f373c9c65 - Pseudo is Norm-conserving + core correction, Zval = 4.0 - Generated using ONCVPSP code by D. R. Hamann - Using radial grid of 1528 points, 6 beta functions with: - l(1) = 0 - l(2) = 0 - l(3) = 1 - l(4) = 1 - l(5) = 2 - l(6) = 2 - - atomic species valence mass pseudopotential - Si 4.00 28.08550 Si( 1.00) - - 48 Sym. Ops., with inversion, found (24 have fractional translation) - - - - Cartesian axes - - site n. atom positions (alat units) - 1 Si tau( 1) = ( 0.0000000 0.0000000 0.0000000 ) - 2 Si tau( 2) = ( 0.2500000 0.2500000 0.2500000 ) - - number of k points= 195 - - Number of k-points >= 100: set verbosity='high' to print them. - - Dense grid: 9257 G-vectors FFT dimensions: ( 32, 32, 32) - - Estimated max dynamical RAM per process > 15.37 MB - - Estimated total dynamical RAM > 54.71 MB - - Initial potential from superposition of free atoms - - starting charge 7.9988, renormalised to 8.0000 - Starting wfcs are 8 randomized atomic wfcs - - total cpu time spent up to now is 0.4 secs - - Self-consistent Calculation - - iteration # 1 ecut= 40.00 Ry beta= 0.70 - Davidson diagonalization with overlap - ethr = 1.00E-02, avg # of iterations = 4.8 - - Threshold (ethr) on eigenvalues was too large: - Diagonalizing with lowered threshold - - Davidson diagonalization with overlap - ethr = 6.54E-04, avg # of iterations = 1.8 - - total cpu time spent up to now is 1.8 secs - - total energy = -16.92187663 Ry - estimated scf accuracy < 0.05593641 Ry - - iteration # 2 ecut= 40.00 Ry beta= 0.70 - Davidson diagonalization with overlap - ethr = 6.99E-04, avg # of iterations = 1.0 - - total cpu time spent up to now is 2.3 secs - - total energy = -16.92416530 Ry - estimated scf accuracy < 0.00216751 Ry - - iteration # 3 ecut= 40.00 Ry beta= 0.70 - Davidson diagonalization with overlap - ethr = 2.71E-05, avg # of iterations = 4.5 - - total cpu time spent up to now is 3.0 secs - - total energy = -16.92427667 Ry - estimated scf accuracy < 0.00003903 Ry - - iteration # 4 ecut= 40.00 Ry beta= 0.70 - Davidson diagonalization with overlap - ethr = 4.88E-07, avg # of iterations = 3.8 - - total cpu time spent up to now is 3.8 secs - - total energy = -16.92428537 Ry - estimated scf accuracy < 0.00000018 Ry - - iteration # 5 ecut= 40.00 Ry beta= 0.70 - Davidson diagonalization with overlap - ethr = 2.31E-09, avg # of iterations = 3.6 - - total cpu time spent up to now is 4.5 secs - - total energy = -16.92428567 Ry - estimated scf accuracy < 0.00000001 Ry - - iteration # 6 ecut= 40.00 Ry beta= 0.70 - Davidson diagonalization with overlap - ethr = 1.32E-10, avg # of iterations = 2.1 - - total cpu time spent up to now is 5.1 secs - - total energy = -16.92428568 Ry - estimated scf accuracy < 2.4E-10 Ry - - iteration # 7 ecut= 40.00 Ry beta= 0.70 - Davidson diagonalization with overlap - ethr = 3.01E-12, avg # of iterations = 2.9 - - total cpu time spent up to now is 5.8 secs - - total energy = -16.92428568 Ry - estimated scf accuracy < 2.3E-11 Ry - - iteration # 8 ecut= 40.00 Ry beta= 0.70 - Davidson diagonalization with overlap - ethr = 2.81E-13, avg # of iterations = 2.3 - - total cpu time spent up to now is 6.4 secs - - total energy = -16.92428568 Ry - estimated scf accuracy < 1.6E-13 Ry - - iteration # 9 ecut= 40.00 Ry beta= 0.70 - Davidson diagonalization with overlap - ethr = 1.00E-13, avg # of iterations = 1.1 - - total cpu time spent up to now is 6.9 secs - - End of self-consistent calculation - - Number of k-points >= 100: set verbosity='high' to print the bands. - - highest occupied, lowest unoccupied level (ev): 6.2319 6.8136 - -! total energy = -16.92428568 Ry - estimated scf accuracy < 8.4E-15 Ry - - The total energy is the sum of the following terms: - one-electron contribution = 4.96250654 Ry - hartree contribution = 1.09491799 Ry - xc contribution = -6.18405500 Ry - ewald contribution = -16.79765521 Ry - - convergence has been achieved in 9 iterations - - Forces acting on atoms (cartesian axes, Ry/au): - - atom 1 type 1 force = 0.00000000 0.00000000 0.00000000 - atom 2 type 1 force = 0.00000000 0.00000000 0.00000000 - - Total force = 0.000000 Total SCF correction = 0.000000 - - - Computing stress (Cartesian axis) and pressure - - total stress (Ry/bohr**3) (kbar) P= 19.79 - 0.00013455 -0.00000000 -0.00000000 19.79 -0.00 -0.00 - -0.00000000 0.00013455 0.00000000 -0.00 19.79 0.00 - -0.00000000 0.00000000 0.00013455 -0.00 0.00 19.79 - - - Writing all to output data dir ./si.save/ : - XML data file, charge density, pseudopotentials, collected wavefunctions - - init_run : 0.25s CPU 0.27s WALL ( 1 calls) - electrons : 5.88s CPU 6.50s WALL ( 1 calls) - forces : 0.12s CPU 0.12s WALL ( 1 calls) - stress : 0.42s CPU 0.42s WALL ( 1 calls) - - Called by init_run: - wfcinit : 0.18s CPU 0.19s WALL ( 1 calls) - potinit : 0.02s CPU 0.04s WALL ( 1 calls) - hinit0 : 0.04s CPU 0.04s WALL ( 1 calls) - - Called by electrons: - c_bands : 4.98s CPU 5.56s WALL ( 10 calls) - sum_band : 0.80s CPU 0.82s WALL ( 10 calls) - v_of_rho : 0.09s CPU 0.10s WALL ( 10 calls) - mix_rho : 0.01s CPU 0.01s WALL ( 10 calls) - - Called by c_bands: - init_us_2 : 0.20s CPU 0.21s WALL ( 1127 calls) - cegterg : 4.24s CPU 4.36s WALL ( 490 calls) - - Called by *egterg: - cdiaghg : 0.09s CPU 0.09s WALL ( 1758 calls) - h_psi : 4.03s CPU 4.14s WALL ( 1856 calls) - g_psi : 0.01s CPU 0.01s WALL ( 1317 calls) - - Called by h_psi: - h_psi:calbec : 0.14s CPU 0.14s WALL ( 1856 calls) - vloc_psi : 3.72s CPU 3.83s WALL ( 1856 calls) - add_vuspsi : 0.15s CPU 0.15s WALL ( 1856 calls) - - General routines - calbec : 0.16s CPU 0.16s WALL ( 2101 calls) - fft : 0.03s CPU 0.04s WALL ( 125 calls) - ffts : 0.00s CPU 0.00s WALL ( 10 calls) - fftw : 3.96s CPU 4.06s WALL ( 25208 calls) - - Parallel routines - - PWSCF : 6.79s CPU 7.53s WALL - - - This run was terminated on: 23: 8:30 17Apr2025 - -=------------------------------------------------------------------------------= - JOB DONE. -=------------------------------------------------------------------------------= diff --git a/Ladygin/test_case/run.sh b/Ladygin/test_case/run.sh deleted file mode 100755 index 31430645..00000000 --- a/Ladygin/test_case/run.sh +++ /dev/null @@ -1,21 +0,0 @@ -#!/bin/bash - -module load python -conda activate tdep -source /pscratch/sd/v/vladygin/doped-Si_project/MLFF_TDEP/testbench/bin/activate - -export SLURM_CPU_BIND="cores" -export OMP_PROC_BIND=spread -export OMP_PLACES=threads -export OMP_NUM_THREADS=1 -export HDF5_USE_FILE_LOCKING=FALSE - -module load PrgEnv-nvidia -module load cuda -module unload cudatoolkit/12.2 -#module load nvhpc/23.1 -module load cray-libsci/23.02.1.1 -module load cray-hdf5-parallel -module swap gpu cpu - -ConvTrack -workdir "./" -mode "qe" -target "total_energy" -eps 0.01 -input "pw.in" -encut 40 diff --git a/Ladygin/tests/reference/pseudo/Si-PBE.upf b/Ladygin/tests/reference/pseudo/Si-PBE.upf new file mode 100644 index 00000000..0bba6c59 --- /dev/null +++ b/Ladygin/tests/reference/pseudo/Si-PBE.upf @@ -0,0 +1,6765 @@ + + + +This pseudopotential file has been produced using the code +ONCVPSP (Optimized Norm-Conservinng Vanderbilt PSeudopotential) +fully-relativistic version 3.3.0 08/16/2017 by D. R. Hamann +The code is available through a link at URL www.mat-simresearch.com. +Documentation with the package provides a full discription of the +input data below. + + +While it is not required under the terms of the GNU GPL, it is +suggested that you cite D. R. Hamann, Phys. Rev. B 88, 085117 (2013) +in any publication using these pseudopotentials. + + +# ATOM AND REFERENCE CONFIGURATION +# atsym z nc nv iexc psfile +Si 14.00 3 2 4 both +# +# n l f energy (Ha) +1 0 2.00 +2 0 2.00 +2 1 6.00 +3 0 2.00 +3 1 2.00 +# +# PSEUDOPOTENTIAL AND OPTIMIZATION +# lmax +2 +# +# l, rc, ep, ncon, nbas, qcut +0 1.60000 -0.39735 4 9 6.40000 +1 1.71000 -0.14959 4 9 6.40000 +2 1.90000 0.05000 4 9 6.40000 +# +# LOCAL POTENTIAL +# lloc, lpopt, rc(5), dvloc0 +4 5 1.60000 0.00000 +# +# VANDERBILT-KLEINMAN-BYLANDER PROJECTORs +# l, nproj, debl +0 2 2.50000 +1 2 2.49961 +2 2 1.50000 +# +# MODEL CORE CHARGE +# icmod, fcfact, rcfact +3 4.00000 1.30000 +# +# LOG DERIVATIVE ANALYSIS +# epsh1, epsh2, depsh +-12.00 12.00 0.02 +# +# OUTPUT GRID +# rlmax, drl +6.00 0.01 +# +# TEST CONFIGURATIONS +# ncnf +0 +# nvcnf +# n l f + + + + + + + + +0.0000 0.0100 0.0200 0.0300 0.0400 0.0500 0.0600 0.0700 +0.0800 0.0900 0.1000 0.1100 0.1200 0.1300 0.1400 0.1500 +0.1600 0.1700 0.1800 0.1900 0.2000 0.2100 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89% rename from Ladygin/test_case/pw.in rename to Ladygin/tests/reference/pw.in index 16e2c495..861a85ce 100644 --- a/Ladygin/test_case/pw.in +++ b/Ladygin/tests/reference/pw.in @@ -4,9 +4,9 @@ wf_collect = .true. tstress = .true. tprnfor = .true. - outdir = './' + outdir = './reference' prefix = 'si' - pseudo_dir = './pseudo' + pseudo_dir = './reference/pseudo' / &SYSTEM nbnd = 8 @@ -29,7 +29,7 @@ ATOMIC_SPECIES Si 28.085 Si-PBE.upf K_POINTS automatic -18 18 18 0 0 0 +26 26 26 0 0 0 CELL_PARAMETERS angstrom -2.71520825606202 0.00000000000000 2.71520825606202 diff --git a/Ladygin/tests/test_convtrack.py b/Ladygin/tests/test_convtrack.py new file mode 100644 index 00000000..8a187a85 --- /dev/null +++ b/Ladygin/tests/test_convtrack.py @@ -0,0 +1,99 @@ +import pytest + + + +def test_loading(): + from ConvergenceTracker import kpoint_scheduler_uniform, mae + from ConvergenceTracker import DriverQE, ConvergenceTrackerQE + from ConvergenceTracker import calculator_qe, calculator_qe_par + +from ConvergenceTracker import kpoint_scheduler_uniform, mae +from ConvergenceTracker import DriverQE, ConvergenceTrackerQE +from ConvergenceTracker import calculator_qe, calculator_qe_par + + + +def test_ksh(): + """Test of sequential kpoint scheduler""" + k_sch = kpoint_scheduler_uniform(2, 30, 2) + for el in range(2, 30, 2): + assert k_sch.get_next() == el + + + +def test_mae_float(): + """Test of mae metrix""" + assert mae()(5, 10) == 5 + + +def test_mae_vec(): + """Test of mae metrix""" + assert int(mae()([2, 4], [4, 8])) == 3 + + + +path_to_exec = "/pscratch/sd/v/vladygin/tools/q-e_new/q-e/bin" + + +def test_driver(): + """Test of sequential sequential driver on Si""" + calculator = calculator_qe(path_to_exec) + driver = DriverQE(workdir = './reference', + calculator = calculator, + input_file_name = "pw.in", + encut = 40) + + driver.gen_input(4) + driver.calc() + total_energy = driver.extract_target('total_energy') + assert int(total_energy // 100) == -3 + + +def test_convergence_tracker_Si_seq(): + """Test of sequential convergence tracker on Si""" + calculator = calculator_qe(path_to_exec) + + driver = DriverQE(workdir = './reference', + calculator = calculator, + input_file_name = "pw.in", + encut = 40) + + tracker = ConvergenceTrackerQE('./reference', 'total_energy', + driver = driver) + + k_list, target_list, errors = tracker.find_opt() + + assert errors[-1] <= 1e-2 or k_list[-1] == 28 + +def test_convergence_tracker_Si_par(): + """Test of parallel convergence tracker on Si""" + calculator = calculator_qe_par(path_to_exec, 1, 1) + + driver = DriverQE(workdir = './reference', + calculator = calculator, + input_file_name = "pw.in", + encut = 40) + + tracker = ConvergenceTrackerQE('./reference', 'total_energy', + driver = driver) + + k_list, target_list, errors = tracker.find_opt() + + assert errors[-1] <= 1e-2 or k_list[-1] == 28 + + +def test_convergence_tracker_Si_par_job(): + """Test of parallel convergence tracker on Si""" + calculator = calculator_qe_par(path_to_exec, 1, 1, job = True, workdir = './reference') + + driver = DriverQE(workdir = './reference', + calculator = calculator, + input_file_name = "pw.in", + encut = 40) + + tracker = ConvergenceTrackerQE('./reference', 'total_energy', + driver = driver) + + k_list, target_list, errors = tracker.find_opt() + + assert errors[-1] <= 1e-2 or k_list[-1] == 28 From a573329a56240c3ee28875e1aab53ea3c17baf3a Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Mon, 21 Apr 2025 22:11:38 -0700 Subject: [PATCH 11/13] Added new version of convergence_trackers --- Ladygin/ConvergenceTracker/convergence_tracker_base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_base.py b/Ladygin/ConvergenceTracker/convergence_tracker_base.py index d5e7a064..e3a062f4 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_base.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_base.py @@ -53,7 +53,7 @@ def _step(self, k_curr: int) -> float: raise NotImplementedError @abstractmethod - def find_opt(self, driver: Driver, k_sch: kpoint_scheduler) -> None: + def find_opt(self) -> None: """Finds optimal kpoint by running simulations and refining the parameter until convergence reached driver - drives for simulations @@ -86,4 +86,4 @@ def __repr__(self) -> str: Tracker.save('TestTracker') Tracker.load() print(Tracker) - os.remove('TestTracker.dat') \ No newline at end of file + os.remove('TestTracker.dat') From 2459ac8020df043b9254c1b0963b5a368dcc1e62 Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Mon, 21 Apr 2025 22:12:47 -0700 Subject: [PATCH 12/13] Removed test inside the file --- Ladygin/ConvergenceTracker/convergence_tracker_base.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/Ladygin/ConvergenceTracker/convergence_tracker_base.py b/Ladygin/ConvergenceTracker/convergence_tracker_base.py index e3a062f4..f49231e8 100644 --- a/Ladygin/ConvergenceTracker/convergence_tracker_base.py +++ b/Ladygin/ConvergenceTracker/convergence_tracker_base.py @@ -77,13 +77,3 @@ def load(self) -> None: def __repr__(self) -> str: return f"Convergence Tracker of {self.target}) with eps equal to {self.eps} with results saved at {self.workdir}" - - -# Usage examples -if __name__ == "__main__": - Tracker = ConvergenceTracker() - print(Tracker) - Tracker.save('TestTracker') - Tracker.load() - print(Tracker) - os.remove('TestTracker.dat') From 3fb806f41f4dc928959ec5298e2acb93f4ead7e3 Mon Sep 17 00:00:00 2001 From: Vladimir Ladygin Date: Mon, 21 Apr 2025 23:02:37 -0700 Subject: [PATCH 13/13] Added final schema --- Ladygin/schema.pdf | Bin 0 -> 49377 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 Ladygin/schema.pdf diff --git a/Ladygin/schema.pdf b/Ladygin/schema.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5732fab76b494c18bd78492c2e66a419ee2d04a6 GIT binary patch literal 49377 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