Open Source Infrastructure for Differentiable Density Functional Theory
This work addresses a software bottleneck for researchers in quantum chemistry, but it is incremental as it adapts existing techniques.
The authors tackled the need for software infrastructure to train neural exchange correlation functionals in quantum chemistry by building open source tools, resulting in the release of a model in the DeepChem library to standardize the processing pipeline.
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.