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.
@article{arxiv.2309.15985,
title = {Open Source Infrastructure for Differentiable Density Functional Theory},
author = {Advika Vidhyadhiraja and Arun Pa Thiagarajan and Shang Zhu and Venkat Viswanathan and Bharath Ramsundar},
journal= {arXiv preprint arXiv:2309.15985},
year = {2023}
}