Update README.md

README.md

@@ -1,8 +1,53 @@
 ---
 license: mit
+tags:
+- Machine Learning Interatomic Potential
 ---
 
-[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). If you use the pretrained models in this repository, please cite all the following:
+# Model Card for mace-unversal
 
-- Batatia, Ilyes, et al. "MACE: Higher order equivariant message passing neural networks for fast and accurate force fields." Advances in Neural Information Processing Systems 35 (2022): 11423-11436.
-- Riebesell, Janosh, et al. "Matbench Discovery--An evaluation framework for machine learning crystal stability prediction." arXiv preprint arXiv:2308.14920 (2023).
+[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 convering 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 }
+}
+```
+
+# 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