Andrewrgarcia

.gitignore

@@ -0,0 +1,12 @@
+__pycache__
+*.egg-info/
+*.eggs/
+*.data
+
+#neuralnetwork-generated
+data
+
+old
+deploy-notes 
+venv
+secret.py

README.md

@@ -1,66 +1,49 @@
-# ReWoTes
+# mlbands
 
-REal WOrld TEstS
+A python package that implements automatic prediction of electronic band gaps for a set of materials based on training data.
 
-## Overview
 
-This repository contains example test assignments used during our hiring process.
+## Installation
 
-We find that regular job interview questions can often be misleading and so use more engaged "real-world" examples instead.
+```ruby
+pip install --upgrade mlbands
+```
 
-Each file represents an assignment similar to what one would get when hired.
+## Documentation and Usage on Google Colab (click below)
 
-| Focus          | ReWote                    | Keywords                        |
-| ---------------| --------------------------| ------------------------------- |
-| Comp. Science  | [Convergence Tracker](Convergence-Tracker.md) | Python, OOD, DFT, Planewaves    |
-| Comp. Science  | [Basis Set Selector](Basis-Set-Selector.md)  | Python, OOD, DFT, Local-orbital |
-| Data. Science  | [ML Property Predict](ML-Band-Gaps.md) | Python, ML Models, Scikit, Featurization |
-| Front-End / UX | [Materials Designer](Materials-Designer.md)  | ReactJS / UX Design, ThreeJS   |
-| Front-End / UX | [Flowchart Designer](Flowchart-Designer.md)  | ReactJS / UX Design, DAG       |
-| Back-End / Ops | [Parallel Uploader](Parallel-File-Uploader.md)   | Python, OOD, Threading, Objectstore |
-| CI/CD, DevOps  | [End-to-End Tests](End-To-End-Tests.md)    | BDD tests, CI/CD workflows, Cypress |
-| HPC, Cloud Inf | [Cloud HPC Bench.](Cloud-Infrastructure.md)    | HPC Cluster, Linpack, Benchmarks |
-| HPC, Containers| [Containerized HPC](Containerization-HPC.md)    | HPC Cluster, Containers, Benchmarks |
+<a href="https://colab.research.google.com/drive/14GS_jUo_B6ojDip-Ak2VsGdrZL1Fgx5a?usp=sharing">
+<img src="https://github.com/andrewrgarcia/powerxrd/blob/main/img/colab.png?raw=true" width="300" ></a>
 
-## Usage
 
-We suggest the following flow:
 
-1. [Fork](https://docs.github.com/en/free-pro-team@latest/github/getting-started-with-github/fork-a-repo) this repository on GitHub
-2. Create a branch using your GitHub username as a branch name
-3. Create a subfolder with your GitHub username
-4. Copy one of the ReWoTe suggestions (`.md` files) to `README.md` in that subfolder and modify the content of the ReWoTe as necessary
-5. Introduce any changes under the subfolder
-6. Submit a [pull request](https://docs.github.com/en/free-pro-team@latest/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork) into the `dev` branch of this repository
+# ML Band Gaps (Materials)
 
-See [dev branch](https://github.com/Exabyte-io/rewotes/tree/dev) also.
+> Ideal candidate: skilled ML data scientist with solid knowledge of materials science.
 
-## Notes
+# Overview
 
-Examples listed here are only meant as guidelines and do not necessarily reflect on the type of work to be performed at the company. Modifications to the individual assignments with an advance notice are encouraged.
+The aim of this task is to create a python package that implements automatic prediction of electronic band gaps for a set of materials based on training data.
 
-We will screen for the ability to (1) pick up new concepts quickly, (2) implement a working proof-of-concept solution, and (3) outline how the PoC can become more mature. We value attention to details and modularity.
+# User story
 
+As a user of this software I can predict the value of an electronic band gap after passing training data and structural information about the target material.
 
-## Hiring process
+# Requirements
 
-Our hiring process in more details:
+- suggest the bandgap values for a set of materials designated by their crystallographic and stoichiometric properties
+- the code shall be written in a way that can facilitate easy addition of other characteristics extracted from simulations (forces, pressures, phonon frequencies etc)
 
-| Stage             | Target Duration   | Topic                          |
-| ----------------- | ----------------- | ------------------------------ |
-| 0. Email screen   |                   | why mat3ra.com / exabyte.io    |
-| 1. Phone screen   | 15-20 min         | career goals, basic skillset   |
-| 2. ReWoTe         | 1-2h x 2-5 days   | real-world work/thought process|
-| 3. On-site meet   | 3-4 x 30 min      | personality fit                |
-| 4. Discuss offer  | 30 min            | cash/equity/benefits           |
-| 5. References     | 2 x 15 min        | sanity check                   |
-| 6. Decision       |                   | when to start                  |
+# Expectations
 
-TOTAL: ~2 weeks tentative.
+- the code shall be able to suggest realistic values for slightly modified geometry sets - eg. trained on Si and Ge it should suggest the value of bandgap for Si49Ge51 to be between those of Si and Ge
+- modular and object-oriented implementation
+- commit early and often - at least once per 24 hours
 
+# Timeline
 
-## Contact info
+We leave exact timing to the candidate. Must fit Within 5 days total.
 
-With any questions about this repository or our hiring process please contact us at info@mat3ra.com.
+# Notes
 
-© 2022 Exabyte Inc.
+- use a designated github repository for version control
+- suggested source of training data: materialsproject.org

draft/GdSnPd.cif

@@ -0,0 +1,38 @@
+# generated using pymatgen
+data_GdSnPd
+_symmetry_space_group_name_H-M   'P 1'
+_cell_length_a   4.65008947
+_cell_length_b   7.31311105
+_cell_length_c   7.95465153
+_cell_angle_alpha   90.00000000
+_cell_angle_beta   90.00000000
+_cell_angle_gamma   90.00000000
+_symmetry_Int_Tables_number   1
+_chemical_formula_structural   GdSnPd
+_chemical_formula_sum   'Gd4 Sn4 Pd4'
+_cell_volume   270.51081729
+_cell_formula_units_Z   4
+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
+  Gd  Gd0  1  0.25000000  0.51062534  0.20448969  1
+  Gd  Gd1  1  0.25000000  0.01062534  0.29551031  1
+  Gd  Gd2  1  0.75000000  0.48937466  0.79551031  1
+  Gd  Gd3  1  0.75000000  0.98937466  0.70448969  1
+  Sn  Sn4  1  0.25000000  0.69150951  0.58737792  1
+  Sn  Sn5  1  0.25000000  0.19150951  0.91262208  1
+  Sn  Sn6  1  0.75000000  0.30849049  0.41262208  1
+  Sn  Sn7  1  0.75000000  0.80849049  0.08737792  1
+  Pd  Pd8  1  0.25000000  0.79706757  0.91570644  1
+  Pd  Pd9  1  0.25000000  0.29706757  0.58429356  1
+  Pd  Pd10  1  0.75000000  0.20293243  0.08429356  1
+  Pd  Pd11  1  0.75000000  0.70293243  0.41570644  1

draft/GdSnPd.json

@@ -0,0 +1,129 @@
+{
+	"@module": "pymatgen.core.structure",
+	"@class": "Structure",
+	"charge": 0,
+	"lattice": {
+		"matrix": [
+			[4.65008947, 0.0, 2.847358592592156e-16],
+			[1.1760379519337024e-15, 7.31311105014398, 4.477989019683982e-16],
+			[0.0, 0.0, 7.954651530160617]
+		],
+		"pbc": [true, true, true],
+		"a": 4.65008947,
+		"b": 7.31311105014398,
+		"c": 7.954651530160617,
+		"alpha": 90.0,
+		"beta": 90.0,
+		"gamma": 90.0,
+		"volume": 270.51081728514777
+	},
+	"sites": [{
+		"species": [{
+			"element": "Gd",
+			"occu": 1
+		}],
+		"abc": [0.25, 0.51062534, 0.2044896850000001],
+		"xyz": [1.1625223675000007, 3.734259816437527, 1.6266441856873135],
+		"label": "Gd",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Gd",
+			"occu": 1
+		}],
+		"abc": [0.25, 0.010625340000000039, 0.2955103149999999],
+		"xyz": [1.1625223675, 0.07770429136553712, 2.350681579392995],
+		"label": "Gd",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Gd",
+			"occu": 1
+		}],
+		"abc": [0.75, 0.48937465999999996, 0.7955103149999999],
+		"xyz": [3.4875671025000003, 3.5788512337064526, 6.328007344473304],
+		"label": "Gd",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Gd",
+			"occu": 1
+		}],
+		"abc": [0.75, 0.98937466, 0.7044896850000001],
+		"xyz": [3.4875671025000012, 7.235406758778443, 5.603969950767622],
+		"label": "Gd",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Sn",
+			"occu": 1
+		}],
+		"abc": [0.25, 0.69150951, 0.5873779150000001],
+		"xyz": [1.162522367500001, 5.057085838860649, 4.672386630337304],
+		"label": "Sn",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Sn",
+			"occu": 1
+		}],
+		"abc": [0.25, 0.19150950999999994, 0.9126220849999999],
+		"xyz": [1.1625223675000003, 1.4005303137886584, 7.259590664903621],
+		"label": "Sn",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Sn",
+			"occu": 1
+		}],
+		"abc": [0.75, 0.30849048999999995, 0.41262208499999986],
+		"xyz": [3.4875671025000003, 2.2560252112833306, 3.2822648998233133],
+		"label": "Sn",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Sn",
+			"occu": 1
+		}],
+		"abc": [0.75, 0.80849049, 0.08737791500000025],
+		"xyz": [3.487567102500001, 5.91258073635532, 0.6950608652569968],
+		"label": "Sn",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Pd",
+			"occu": 1
+		}],
+		"abc": [0.25, 0.79706757, 0.915706435],
+		"xyz": [1.162522367500001, 5.82904365387841, 7.284125594350674],
+		"label": "Pd",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Pd",
+			"occu": 1
+		}],
+		"abc": [0.25, 0.29706756999999984, 0.584293565],
+		"xyz": [1.1625223675000005, 2.172488128806419, 4.647851700890252],
+		"label": "Pd",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Pd",
+			"occu": 1
+		}],
+		"abc": [0.75, 0.20293243000000005, 0.08429356499999996],
+		"xyz": [3.4875671025000003, 1.4840673962655702, 0.6705259358099435],
+		"label": "Pd",
+		"properties": {}
+	}, {
+		"species": [{
+			"element": "Pd",
+			"occu": 1
+		}],
+		"abc": [0.75, 0.70293243, 0.41570643500000015],
+		"xyz": [3.487567102500001, 5.14062292133756, 3.306799829270367],
+		"label": "Pd",
+		"properties": {}
+	}]
+}