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--- |
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license: mit |
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tags: |
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- Machine Learning Interatomic Potential |
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--- |
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# Model Card for mace-universal |
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[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). |
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[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). |
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# Usage |
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1. (optional) Install Pytorch, [ASE](https://wiki.fysik.dtu.dk/ase/) prerequisites for specific version |
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2. Install [MACE](https://github.com/ACEsuit/mace) through GitHub (not through pypi) |
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```shell |
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pip install git+https://github.com/ACEsuit/mace.git |
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``` |
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3. Use MACECalculator |
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```python |
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from mace.calculators import MACECalculator |
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from ase.md.npt import NPT |
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calculator = MACECalculator( |
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model_paths=/path/to/pretrained.model, |
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device=device |
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) |
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nvt = NPT( |
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atoms=atoms, |
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timestep=timestep, |
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temperature_K=temperature, |
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externalstress=externalstress, |
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) |
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``` |
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# Citing |
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If you use the pretrained models in this repository, please cite all the following: |
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``` |
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@inproceedings{Batatia2022mace, |
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title={{MACE}: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields}, |
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author={Ilyes Batatia and David Peter Kovacs and Gregor N. C. Simm and Christoph Ortner and Gabor Csanyi}, |
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booktitle={Advances in Neural Information Processing Systems}, |
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editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho}, |
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year={2022}, |
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url={https://openreview.net/forum?id=YPpSngE-ZU} |
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} |
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@article{riebesell2023matbench, |
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title={Matbench Discovery--An evaluation framework for machine learning crystal stability prediction}, |
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author={Riebesell, Janosh and Goodall, Rhys EA and Jain, Anubhav and Benner, Philipp and Persson, Kristin A and Lee, Alpha A}, |
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journal={arXiv preprint arXiv:2308.14920}, |
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year={2023} |
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} |
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@misc {yuan_chiang_2023, |
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author = { {Yuan Chiang} }, |
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title = { mace-universal (Revision e5ebd9b) }, |
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year = 2023, |
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url = { https://huggingface.co/cyrusyc/mace-universal }, |
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doi = { 10.57967/hf/1202 }, |
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publisher = { Hugging Face } |
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} |
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@article{deng2023chgnet, |
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title={CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling}, |
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author={Deng, Bowen and Zhong, Peichen and Jun, KyuJung and Riebesell, Janosh and Han, Kevin and Bartel, Christopher J and Ceder, Gerbrand}, |
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journal={Nature Machine Intelligence}, |
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pages={1--11}, |
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year={2023}, |
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publisher={Nature Publishing Group UK London} |
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} |
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``` |
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# Training Details |
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## Training Data |
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<!-- 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. --> |
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## Training Procedure |
