Updates bibtex entries for GemNet-OC, SCN, PaiNN (#420)

ocpmodels/models/gemnet_oc/README.md

@@ -1,4 +1,4 @@
-# How Do Graph Networks Generalize to Large and Diverse Molecular Systems?
+# GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
 
 Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ulissi, C. Lawrence Zitnick, Abhishek Das
 
@@ -21,9 +21,9 @@ If you use GemNet-OC in your work, please consider citing:
 
 ```bibtex
 @article{gasteiger_gemnet_oc_2022,
-  title={{How Do Graph Networks Generalize to Large and Diverse Molecular Systems?}},
-  author={Gasteiger, Johannes and Shuaibi, Muhammed and Sriram, Anuroop and G{\"u}nnemann, Stephan and Ulissi, Zachary and Zitnick, C Lawrence and Das, Abhishek},
-  journal={arXiv preprint arXiv:2204.02782},
-  year={2022}
+  title = {{GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets}},
+  author = {Gasteiger, Johannes and Shuaibi, Muhammed and Sriram, Anuroop and G{\"u}nnemann, Stephan and Ulissi, Zachary and Zitnick, C Lawrence and Das, Abhishek},
+  journal = {Transactions on Machine Learning Research (TMLR)},
+  year = {2022},
 }
 ```

ocpmodels/models/painn/README.md

@@ -24,3 +24,16 @@ Trained only using IS2RE data, no auxiliary losses and/or S2EF data.
 | Model | Val ID 30k Force MAE | Val ID 30k Energy MAE | Val ID 30k Force cos | Test metrics | Download |
 | ----- | -------------------- | --------------------- | -------------------- | ------------ | -------- |
 | painn_h512 | 0.02945 | 0.2459 | 0.5143 | [S2EF](https://evalai.s3.amazonaws.com/media/submission_files/submission_200711/2f487981-051d-445e-a7cd-6eb00ebe0735.json) \| [IS2RE](https://evalai.s3.amazonaws.com/media/submission_files/submission_200710/7fe29c4c-c203-434d-a6d4-9ea992d3bb5c.json) \| [IS2RS](https://evalai.s3.amazonaws.com/media/submission_files/submission_200700/8fd419e6-bab3-49be-a936-ae31979b4866.json) | [config](https://github.com/Open-Catalyst-Project/ocp/tree/main/configs/s2ef/all/painn/painn_h512.yml) \| [checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_05/s2ef/painn_h512_s2ef_all.pt) |
+
+## Citing
+
+If you use PaiNN in your work, please consider citing the original paper:
+
+```bibtex
+@inproceedings{schutt_painn_2021,
+  title = {Equivariant message passing for the prediction of tensorial properties and molecular spectra},
+  author = {Sch{\"u}tt, Kristof and Unke, Oliver and Gastegger, Michael},
+  booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
+  year = {2021},
+}
+```

ocpmodels/models/scn/README.md

@@ -7,3 +7,16 @@ C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi,
 To run the Spherical Channel Network (SCN), install [e3nn](https://github.com/e3nn/e3nn/) with `pip install e3nn==0.2.6`.
 
 SCN was developed with e3nn v0.2.6, and might run slower with later versions [[1](https://github.com/Open-Catalyst-Project/ocp/issues/397), [2](https://github.com/Open-Catalyst-Project/ocp/pull/402)].
+
+## Citing
+
+If you use SCN in your work, please consider citing:
+
+```bibtex
+@inproceedings{zitnick_scn_2022,
+  title = {{Spherical Channels for Modeling Atomic Interactions}},
+  author = {Zitnick, C. Lawrence and Das, Abhishek and Kolluru, Adeesh and Lan, Janice and Shuaibi, Muhammed and Sriram, Anuroop and Ulissi, Zachary and Wood, Brandon},
+  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
+  year = {2022},
+}
+```