- About MACE
- Documentation
- Installation
- Usage
- Tutorial
- Weights and Biases
- Development
- Pretrained models
- References
- Contact
- License
MACE provides fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
This repository contains the MACE reference implementation developed by Ilyes Batatia, Gregor Simm, and David Kovacs.
Also available:
- MACE in JAX, currently about 2x times faster at evaluation, but training is recommended in Pytorch for optimal performances.
- MACE layers for constructing higher order equivariant graph neural networks for arbitrary 3D point clouds.
A partial documentation is available at: https://mace-docs.readthedocs.io
Requirements:
- Python >= 3.7
- PyTorch >= 1.12 (2.1 is not supported, 2.0 is recommended)
(for openMM, use Python = 3.9)
If you do not have CUDA pre-installed, it is recommended to follow the conda installation process:
# Create a virtual environment and activate it
conda create --name mace_env
conda activate mace_env
# Install PyTorch
conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
# (optional) Install MACE's dependencies from Conda as well
conda install numpy scipy matplotlib ase opt_einsum prettytable pandas e3nn
# Clone and install MACE (and all required packages)
git clone https://github.com/ACEsuit/mace.git
pip install ./maceTo install via pip, follow the steps below:
# Create a virtual environment and activate it
python -m venv mace-venv
source mace-venv/bin/activate
# Install PyTorch (for example, for CUDA 11.6 [cu116])
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
# Clone and install MACE (and all required packages)
git clone https://github.com/ACEsuit/mace.git
pip install ./maceNote: The homonymous package on PyPI has nothing to do with this one.
To train a MACE model, you can use the mace_run_train script, which should be in the usual place that pip places binaries (or you can explicitly run python3 <path_to_cloned_dir>/mace/cli/run_train.py)
mace_run_train \
--name="MACE_model" \
--train_file="train.xyz" \
--valid_fraction=0.05 \
--test_file="test.xyz" \
--config_type_weights='{"Default":1.0}' \
--E0s='{1:-13.663181292231226, 6:-1029.2809654211628, 7:-1484.1187695035828, 8:-2042.0330099956639}' \
--model="MACE" \
--hidden_irreps='128x0e + 128x1o' \
--r_max=5.0 \
--batch_size=10 \
--max_num_epochs=1500 \
--swa \
--start_swa=1200 \
--ema \
--ema_decay=0.99 \
--amsgrad \
--restart_latest \
--device=cuda \To give a specific validation set, use the argument --valid_file. To set a larger batch size for evaluating the validation set, specify --valid_batch_size.
To control the model's size, you need to change --hidden_irreps. For most applications, the recommended default model size is --hidden_irreps='256x0e' (meaning 256 invariant messages) or --hidden_irreps='128x0e + 128x1o'. If the model is not accurate enough, you can include higher order features, e.g., 128x0e + 128x1o + 128x2e, or increase the number of channels to 256.
It is usually preferred to add the isolated atoms to the training set, rather than reading in their energies through the command line like in the example above. To label them in the training set, set config_type=IsolatedAtom in their info fields. If you prefer not to use or do not know the energies of the isolated atoms, you can use the option --E0s="average" which estimates the atomic energies using least squares regression.
If the keyword --swa is enabled, the energy weight of the loss is increased for the last ~20% of the training epochs (from --start_swa epochs). This setting usually helps lower the energy errors.
The precision can be changed using the keyword --default_dtype, the default is float64 but float32 gives a significant speed-up (usually a factor of x2 in training).
The keywords --batch_size and --max_num_epochs should be adapted based on the size of the training set. The batch size should be increased when the number of training data increases, and the number of epochs should be decreased. An heuristic for initial settings, is to consider the number of gradient update constant to 200 000, which can be computed as
The code can handle training set with heterogeneous labels, for example containing both bulk structures with stress and isolated molecules. In this example, to make the code ignore stress on molecules, append to your molecules configuration a config_stress_weight = 0.0.
To use Apple Silicon GPU acceleration make sure to install the latest PyTorch version and specify --device=mps.
To evaluate your MACE model on an XYZ file, run the mace_eval_configs:
mace_eval_configs \
--configs="your_configs.xyz" \
--model="your_model.model" \
--output="./your_output.xyz"You can run our Colab tutorial to quickly get started with MACE. We also have a more detailed user and developer tutorial at https://github.com/ilyes319/mace-tutorials
If you would like to use MACE with Weights and Biases to log your experiments simply install with
pip install ./mace[wandb]And specify the necessary keyword arguments (--wandb, --wandb_project, --wandb_entity, --wandb_name, --wandb_log_hypers)
We use black, isort, pylint, and mypy.
Run the following to format and check your code:
bash ./scripts/run_checks.shWe have CI set up to check this, but we highly recommend that you run those commands before you commit (and push) to avoid accidentally committing bad code.
We are happy to accept pull requests under an MIT license. Please copy/paste the license text as a comment into your pull request.
We have collaborated with the Materials Project (MP) who trained universal MACE checkpoints covering 89 elements on 1.6 M bulk crystals in the MPTrj dataset selected from MP relaxation trajectories. These pretrained models were used for materials stability prediction in Matbench Discovery and the corresponding preprint. For easy reuse, these checkpoints were published on Hugging Face and Figshare with direct download links for the medium and large checkpoints.
If you use this code, please cite our papers:
@inproceedings{Batatia2022mace,
title={{MACE}: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields},
author={Ilyes Batatia and David Peter Kovacs and Gregor N. C. Simm and Christoph Ortner and Gabor Csanyi},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=YPpSngE-ZU}
}
@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}
}
If you have any questions, please contact us at ilyes.batatia@ens-paris-saclay.fr.
For bugs or feature requests, please use GitHub Issues.
MACE is published and distributed under the MIT License.