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---
license: mit
tags:
- Machine Learning Interatomic Potential
---
# Model Card for mace-universal
[MACE](https://github.com/ACEsuit/mace) (Multiple Atomic Cluster Expansion) is a machine learning interatomic potential (MLIP) with higher order equivariant message passing. For more information about MACE formalism, please see authors' [paper](https://arxiv.org/abs/2206.07697).
[2023-08-14-mace-universal.model](https://huggingface.co/cyrusyc/mace-universal/blob/main/2023-08-14-mace-universal.model) was trained with MPTrj data, [Materials Project](https://materialsproject.org) relaxation trajectories compiled by [CHGNet](https://arxiv.org/abs/2302.14231) authors to cover 89 elements and 1.6M configurations. The checkpoint was used for materials stability prediction in [Matbench Discovery](https://matbench-discovery.materialsproject.org/) and the corresponding [preprint](https://arXiv.org/abs/2308.14920).
# Usage
1. (optional) Install Pytorch, [ASE](https://wiki.fysik.dtu.dk/ase/) prerequisites for specific version
2. Install [MACE](https://github.com/ACEsuit/mace) through GitHub (not through pypi)
```shell
pip install git+https://github.com/ACEsuit/mace.git
```
3. Use MACECalculator
```python
from mace.calculators import MACECalculator
from ase.md.npt import NPT
calculator = MACECalculator(
model_paths=/path/to/pretrained.model,
device=device
)
nvt = NPT(
atoms=atoms,
timestep=timestep,
temperature_K=temperature,
externalstress=externalstress,
)
```
# Citing
If you use the pretrained models in this repository, please cite all the following:
```
@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}
}
@article{riebesell2023matbench,
title={Matbench Discovery--An evaluation framework for machine learning crystal stability prediction},
author={Riebesell, Janosh and Goodall, Rhys EA and Jain, Anubhav and Benner, Philipp and Persson, Kristin A and Lee, Alpha A},
journal={arXiv preprint arXiv:2308.14920},
year={2023}
}
@misc {yuan_chiang_2023,
author = { {Yuan Chiang} },
title = { mace-universal (Revision e5ebd9b) },
year = 2023,
url = { https://huggingface.co/cyrusyc/mace-universal },
doi = { 10.57967/hf/1202 },
publisher = { Hugging Face }
}
@article{deng2023chgnet,
title={CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling},
author={Deng, Bowen and Zhong, Peichen and Jun, KyuJung and Riebesell, Janosh and Han, Kevin and Bartel, Christopher J and Ceder, Gerbrand},
journal={Nature Machine Intelligence},
pages={1--11},
year={2023},
publisher={Nature Publishing Group UK London}
}
```
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
## Training Procedure |