fairchem is the FAIR Chemistry's centralized repository of all its data, models, demos, and application efforts for materials science and quantum chemistry.
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:
- 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.
fairchem is released under the MIT license.
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},
}