• MACE
  • Table of contents
  • About MACE
  • Documentation
  • Installation
    • pip installation
    • pip installation from source
  • Usage
    • Training
    • Evaluation
  • Tutorials
  • Weights and Biases for experiment tracking
  • Pretrained Foundation Models
    • MACE-MP: Materials Project Force Fields
      • Example usage in ASE
    • MACE-OFF: Transferable Organic Force Fields
      • Example usage in ASE
    • Finetuning foundation models
  • Development
  • 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, David Kovacs, and the group of Gabor Csanyi, and friends (see Contributors).

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

• Python >= 3.7 (for openMM, use Python = 3.9)
• PyTorch >= 1.12 (training with float64 is not supported with PyTorch 2.1 but is supported with 2.2 and later, Pytorch 2.4.1 is not supported)

Make sure to install PyTorch. Please refer to the official PyTorch installation for the installation instructions. Select the appropriate options for your system.

This is the recommended way to install MACE.

pip install --upgrade pip
pip install mace-torch

Note: The homonymous package on PyPI has nothing to do with this one.

git clone https://github.com/ACEsuit/mace.git
pip install ./mace

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 also possible to specify the model using the --num_channels=128 and --max_L=1keys.

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 $\text{max-num-epochs}*\frac{\text{num-configs-training}}{\text{batch-size}}$.

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.

For multi-GPU training, use the --distributed flag. This will use PyTorch's DistributedDataParallel module to train the model on multiple GPUs. Combine with on-line data loading for large datasets (see below). An example slurm script can be found in mace/scripts/distributed_example.sbatch.

Option to parse all or some arguments using a YAML is available. For example, to train a model using the arguments above, you can create a YAML file your_configs.yaml with the following content:

name: nacl
seed: 2024
train_file: train.xyz
swa: yes
start_swa: 1200
max_num_epochs: 1500
device: cpu
test_file: test.xyz
E0s:
  41: -1029.2809654211628
  38: -1484.1187695035828
  8: -2042.0330099956639
config_type_weights:
  Default: 1.0

And append to the command line --config="your_configs.yaml". Any argument specified in the command line will overwrite the one in the YAML file.

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 Colab tutorials on:

• Introduction to MACE training and evaluation
• Introduction to MACE active learning and fine-tuning
• MACE theory and code (advanced)

If you have a large dataset that might not fit into the GPU memory it is recommended to preprocess the data on a CPU and use on-line dataloading for training the model. To preprocess your dataset specified as an xyz file run the preprocess_data.py script. An example is given here:

mkdir processed_data
python ./mace/scripts/preprocess_data.py \
    --train_file="/path/to/train_large.xyz" \
    --valid_fraction=0.05 \
    --test_file="/path/to/test_large.xyz" \
    --atomic_numbers="[1, 6, 7, 8, 9, 15, 16, 17, 35, 53]" \
    --r_max=4.5 \
    --h5_prefix="processed_data/" \
    --compute_statistics \
    --E0s="average" \
    --seed=123 \

To see all options and a little description of them run python ./mace/scripts/preprocess_data.py --help . The script will create a number of HDF5 files in the processed_data folder which can be used for training. There will be one folder for training, one for validation and a separate one for each config_type in the test set. To train the model use the run_train.py script as follows:

python ./mace/scripts/run_train.py \
    --name="MACE_on_big_data" \
    --num_workers=16 \
    --train_file="./processed_data/train.h5" \
    --valid_file="./processed_data/valid.h5" \
    --test_dir="./processed_data" \
    --statistics_file="./processed_data/statistics.json" \
    --model="ScaleShiftMACE" \
    --num_interactions=2 \
    --num_channels=128 \
    --max_L=1 \
    --correlation=3 \
    --batch_size=32 \
    --valid_batch_size=32 \
    --max_num_epochs=100 \
    --swa \
    --start_swa=60 \
    --ema \
    --ema_decay=0.99 \
    --amsgrad \
    --error_table='PerAtomMAE' \
    --device=cuda \
    --seed=123 \

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 have collaborated with the Materials Project (MP) to train a universal MACE potential covering 89 elements on 1.6 M bulk crystals in the MPTrj dataset selected from MP relaxation trajectories. The models are releaed on GitHub at https://github.com/ACEsuit/mace-mp. If you use them please cite our paper which also contains an large range of example applications and benchmarks.

from mace.calculators import mace_mp
from ase import build

atoms = build.molecule('H2O')
calc = mace_mp(model="medium", dispersion=False, default_dtype="float32", device='cuda')
atoms.calc = calc
print(atoms.get_potential_energy())

There is a series (small, medium, large) transferable organic force fields. These can be used for the simulation of organic molecules, crystals and molecular liquids, or as a starting point for fine-tuning on a new dataset. The models are released under the ASL license. The models are releaed on GitHub at https://github.com/ACEsuit/mace-off. If you use them please cite our paper which also contains detailed benchmarks and example applications.

from mace.calculators import mace_off
from ase import build

atoms = build.molecule('H2O')
calc = mace_off(model="medium", device='cuda')
atoms.calc = calc
print(atoms.get_potential_energy())

To finetune one of the mace-mp-0 foundation model, you can use the mace_run_train script with the extra argument --foundation_model=model_type. For example to finetune the small model on a new dataset, you can use:

mace_run_train \
  --name="MACE" \
  --foundation_model="small" \
  --train_file="train.xyz" \
  --valid_fraction=0.05 \
  --test_file="test.xyz" \
  --energy_weight=1.0 \
  --forces_weight=1.0 \
  --E0s="average" \
  --lr=0.01 \
  --scaling="rms_forces_scaling" \
  --batch_size=2 \
  --max_num_epochs=6 \
  --ema \
  --ema_decay=0.99 \
  --amsgrad \
  --default_dtype="float32" \
  --device=cuda \
  --seed=3 

Other options are "medium" and "large", or the path to a foundation model. If you want to finetune another model, the model will be loaded from the path provided --foundation_model=$path_model, but you will need to provide the full set of hyperparameters (hidden irreps, r_max, etc.) matching the model.

This project uses pre-commit to execute code formatting and linting on commit. We also use black, isort, pylint, and mypy. We recommend setting up your development environment by installing the dev packages into your python environment:

pip install -e ".[dev]"
pre-commit install

The second line will initialise pre-commit to automaticaly run code checks on commit. We 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.

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 [email protected].

For bugs or feature requests, please use GitHub Issues.

MACE is published and distributed under the MIT License.