Bump mace-torch from 0.3.12 to 0.3.15

[dependabot]

Bumps mace-torch from 0.3.12 to 0.3.15.

Release notes

Sourced from mace-torch's releases.

v0.3.15

MACE v0.3.15 Release Notes

We are excited to announce MACE v0.3.15, featuring two new cross-domain foundation models, LoRA fine-tuning, weight freezing, improved LAMMPS MLIAP support for non-linear models, and a range of bug fixes and training improvements.

🏗️ Foundation Models

MACE-MH-1

Introduced MACE-MH-1, a state-of-the-art multi-head foundation machine-learning interatomic potential that unifies molecular, surface, and inorganic crystal chemistry in a single model. MACE-MH-1 covers 89 chemical elements, achieves state of the art accuracy across solids, molecular systems and surfaces, and is built on an enhanced MACE architecture with improved weight sharing and non-linear tensor decomposition.

MACE-MH-1 is pre-trained on OMAT-24 (100M inorganic crystal configurations) and fine-tuned with six heads:

• omat_pbe (default) — PBE/PBE+U, general inorganic materials
• omol — ωB97M-VV10, organic and organometallic molecules
• spice_wB97M — ωB97M-D3(BJ), molecular systems
• rgd1_b3lyp — B3LYP, reaction chemistry intermediates
• oc20_usemppbe — PBE, surface catalysis
• matpes_r2scan — r²SCAN, high-accuracy inorganic materials

MACE-MH-1 is supported in LAMMPS via the MLIAP interface, including support for non-linear interaction blocks (see MLIAP section below). Note that MACE-MH-1 is not yet supported in SYMMETRIX.

Example usage:

from mace.calculators import mace_mp
calc = mace_mp(
model="mh-1",
default_dtype="float64",
device="cuda",
head="omat_pbe",
)
energy = atoms.get_potential_energy()
forces = atoms.get_forces()

Model weights and documentation: Hugging Face — mace-mh-1.

Reference: Batatia et al., "Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields", arXiv:2510.25380

MACE-MH-0

Added MACE-MH-0, the predecessor of MACE-MH-1. It covers the same 89 elements and is trained on the same family of datasets (OMAT, OMOL, OC20, MATPES), providing strong cross-domain performance on bulk, surfaces, and molecules. MACE-MH-0 is supported in SYMMETRIX and via LAMMPS MLIAP.

Example usage:

from mace.calculators import mace_mp
calc = mace_mp(model="mh-0", default_dtype="float64", device="cuda")

... (truncated)

Commits

• a2803e5 Merge pull request #1361 from ACEsuit/develop
• fa47487 Merge pull request #1374 from ThomasWarford/bugfix_eval_configs_head
• 24bcc67 fix heads bug
• c09764f Merge pull request #1341 from ThomasWarford/bugfix_compile_test
• 959920b Merge pull request #1297 from vue1999/merge-lora-weights
• b020906 Merge pull request #1354 from kush2803/patch-1
• 516257c Merge pull request #1352 from Student204161/new_pandas_support
• 966a54d fix precommit checks
• c0fcb16 move lora code to modules
• 7418be2 fix python 3.8 compatibility
• Additional commits viewable in compare view

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