fairchem by FAIR Chemistry

FAIR-Chem overview

fairchem is the FAIR Chemistry’s centralized repository of all its data, models, demos, and application efforts for materials science and quantum chemistry. Collaborative projects that contribute or use the models and approaches in this repo:

• Open Catalyst Project (OCP)

• Open Direct Air Capture (OpenDAC)

Note

We re-organized and rebranded the repository in 2024 (previously the fairchem repo) to reflect the increasingly general usability of these models beyond catalysis, including things like direct air capture.

Datasets in fairchem:

fairchem provides training and evaluation code for tasks and models that take arbitrary chemical structures as input to predict energies / forces / positions / stresses, and can be used as a base scaffold for research projects. For an overview of tasks, data, and metrics, please read the documentations and respective papers:

• OC20

• OC22

• ODAC23

• OC20Dense

• OC20NEB

Projects and models built on fairchem:

• SchNet [arXiv] [code]

• DimeNet++ [arXiv] [code]

• GemNet-dT [arXiv] [code]

• PaiNN [arXiv] [code]

• Graph Parallelism [arXiv] [code]

• GemNet-OC [arXiv] [code]

• SCN [arXiv] [code]

• AdsorbML [arXiv] [code]

• eSCN [arXiv] [code]

• EquiformerV2 [arXiv] [code]

Older model implementations that are no longer supported:

• CGCNN [arXiv] [code]

• DimeNet [arXiv] [code]

• SpinConv [arXiv] [code]

• ForceNet [arXiv] [code]

Discussion

For all non-codebase related questions and to keep up-to-date with the latest OCP announcements, please join the discussion board.

All code-related questions and issues should be posted directly on our issues page.

Acknowledgements

• This codebase was initially forked from CGCNN by Tian Xie, but has undergone significant changes since.

• A lot of engineering ideas have been borrowed from github.com/facebookresearch/mmf.

• The DimeNet++ implementation is based on the author’s Tensorflow implementation and the DimeNet implementation in Pytorch Geometric.

• It was then developed as the OCP repo, and includes many contributions from the community and collaborators.

• Much of the documentation was developed for various papers or as part of a comprehensive tutorial for the 2023 ACS Fall Chemistry conference.

License

fairchem is released under the MIT license.

Citing fairchem

If you use this codebase in your work, please consider citing:

@article{ocp_dataset,
    author = {Chanussot*, Lowik and Das*, Abhishek and Goyal*, Siddharth and Lavril*, Thibaut and Shuaibi*, Muhammed and Riviere, Morgane and Tran, Kevin and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Palizhati, Aini and Sriram, Anuroop and Wood, Brandon and Yoon, Junwoong and Parikh, Devi and Zitnick, C. Lawrence and Ulissi, Zachary},
    title = {Open Catalyst 2020 (OC20) Dataset and Community Challenges},
    journal = {ACS Catalysis},
    year = {2021},
    doi = {10.1021/acscatal.0c04525},
}