Add CHGNet to citation

README.md

@@ -9,7 +9,7 @@ tags:
 [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).
+[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
@@ -33,6 +33,7 @@ If you use the pretrained models in this repository, please cite all the followi
   year={2023}
 }
 
+
 @misc {yuan_chiang_2023,
   author       = { {Yuan Chiang} },
   title        = { mace-universal (Revision e5ebd9b) },
@@ -41,6 +42,15 @@ If you use the pretrained models in this repository, please cite all the followi
   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