Open Source Infrastructure for Differentiable Density Functional Theory
Abstract
Learning exchange correlation functionals, used in quantum chemistry calculations, from data has become increasingly important in recent years, but training such a functional requires sophisticated software infrastructure. For this reason, we build open source infrastructure to train neural exchange correlation functionals. We aim to standardize the processing pipeline by adapting state-of-the-art techniques from work done by multiple groups. We have open sourced the model in the DeepChem library to provide a platform for additional research on differentiable quantum chemistry methods.
Cite
Text
Vidhyadhiraja et al. "Open Source Infrastructure for Differentiable Density Functional Theory." ICML 2023 Workshops: SynS_and_ML, 2023.Markdown
[Vidhyadhiraja et al. "Open Source Infrastructure for Differentiable Density Functional Theory." ICML 2023 Workshops: SynS_and_ML, 2023.](https://mlanthology.org/icmlw/2023/vidhyadhiraja2023icmlw-open/)BibTeX
@inproceedings{vidhyadhiraja2023icmlw-open,
title = {{Open Source Infrastructure for Differentiable Density Functional Theory}},
author = {Vidhyadhiraja, Advika and Thiagarajan, Arun Pa and Zhu, Shang and Viswanathan, Venkatasubraman and Ramsundar, Bharath},
booktitle = {ICML 2023 Workshops: SynS_and_ML},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/vidhyadhiraja2023icmlw-open/}
}