Machine learning models for use in catalysis as part of the Open Catalyst Project
Project description
fairchem
by FAIR Chemistry
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:
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.
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},
}
MIT License
Copyright (c) Facebook, Inc. and its affiliates.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for fairchem_core-1.1.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9ccc20b2ec7fd3ce7205622f903f848c4eb24a866faba49277970a7b8cdd1834 |
|
MD5 | 2703ddf932719765aa8da64f2a11d20e |
|
BLAKE2b-256 | 886fb5b186cf368801bb5ccb631bf265d26aba7db8c56f3c4c5331ca6e53e753 |