Awesome
MACE :rocket: JAX
This repository contains a porting of MACE in jax developed by Mario Geiger and Ilyes Batatia.
Test without installing
pip install nox
nox
Installation
From github:
pip install git+https://github.com/ACEsuit/mace-jax
Or locally:
python setup.py develop
Usage
Training
To train a MACE model, you can use the run_train.py
script:
python -m mace_jax.run_train config.gin
An example of configuration file is located in the directory configs
.
Configuration
Links to the files containing the functions configured by the gin config file.
flags
logs
datasets
model
and hereloss
ifloss.energy_weight
,loss.forces_weight
orloss.stress_weight
is nonzero the loss will be appliedoptimizer
train
Contributions
We are happy to accept pull requests under an MIT license. Please copy/paste the license text as a comment into your pull request.
References
If you use this code, please cite our papers:
@misc{Batatia2022MACE,
title = {MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields},
author = {Batatia, Ilyes and Kov{\'a}cs, D{\'a}vid P{\'e}ter and Simm, Gregor N. C. and Ortner, Christoph and Cs{\'a}nyi, G{\'a}bor},
year = {2022},
number = {arXiv:2206.07697},
eprint = {2206.07697},
eprinttype = {arxiv},
doi = {10.48550/ARXIV.2206.07697},
archiveprefix = {arXiv}
}
@misc{Batatia2022Design,
title = {The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials},
author = {Batatia, Ilyes and Batzner, Simon and Kov{\'a}cs, D{\'a}vid P{\'e}ter and Musaelian, Albert and Simm, Gregor N. C. and Drautz, Ralf and Ortner, Christoph and Kozinsky, Boris and Cs{\'a}nyi, G{\'a}bor},
year = {2022},
number = {arXiv:2205.06643},
eprint = {2205.06643},
eprinttype = {arxiv},
doi = {10.48550/arXiv.2205.06643},
archiveprefix = {arXiv}
}
Contact
If you have any questions, please contact us at ilyes.batatia@ens-paris-saclay.fr or geiger.mario@gmail.com.
License
MACE is published and distributed under the MIT.