v0.3.11

MACE 0.3.11 Release Notes

Versions support updates

• Added support for new version of cuEquivariance 0.2.0 and 0.3.0 (breaking support for 0.1.0). (See https://mace-docs.readthedocs.io/en/latest/guide/cuda_acceleration.html)
• Added support for Pytorch 2.6.0.

Enhanced Dataset Processing

• Added support for LMDB data format for efficient large dataset handling (like fairchem datasets). Supports folders of .aselmdb or .aselmdb files. (See https://mace-docs.readthedocs.io/en/latest/guide/multipreprocessing.html#lmdb-files-preprocessing).

# Example configuration for LMDB
train_file: "/path/data1/:/path/data2/:/path/data3/"
valid_file: "/path/valid1.lmdb:/path/valid2.lmdb"

• Streamlined workflow for training on multiple datasets simultaneously. Can now add lists of different datastream (ASE, lmdb and xyz). (See https://mace-docs.readthedocs.io/en/latest/guide/heterogeneous_data.html).

# Example configuration for different datasets
heads:
  QM7:
    train_file: ["data/qm7/train.xyz", "data/qm7/train.h5", "data/qm7/database/"]
    valid_file: ["data/qm7/valid.xyz", "data/qm7_h5/valid/"]
    test_file:  ["data/qm7/test.xyz", "data/qm7/test.h5"]

New Foundation Models

• MACE-MPA-0: Improved accuracy for materials. New default foundation model when calling mace_mp().
• MACE-OMAT-0: New model trained on the OMAT dataset with DFT (PBE+U) VASP 54.

Plotting training

• Add automatic plotting of training, validation and test statistics after a training run. Two arguments added to mace_run_train command line interface:

--plot # activate or not the plotting, default True
--plot_frequency # If zero only at the end, else, each N epoch

Support for higher L and max_ell

• Switch to cuequivariance generalized CG for L, max_ell or correlation that needs to use CG that were not pre computed in e3nn 0.4.4. Now can train models with arbitrary L and max_ell.

Installation

pip install --upgrade mace-torch

# For CUDA acceleration (CUDA 12)
pip install cuequivariance cuequivariance-torch cuequivariance-ops-torch-cu12
# For CUDA 11, use cuequivariance-ops-torch-cu11 instead

Please see our documentation for complete details on features and usage. If you encounter any issues, please report them on our GitHub Issues page.

Full Changelog: v0.3.10...v0.3.11