---
license: mit
tags:
- Machine Learning Interatomic Potential
---

# Model Card for mace-unversal

[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).


# Citation

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