Differentiable quantum chemistry with PySCF for molecules and materials at the mean-field level and beyond

TL;DR

This paper presents an extension to the PySCF quantum chemistry package enabling automatic differentiation for various molecular and material properties, facilitating advanced methodology development beyond mean-field approximations.

Contribution

The authors introduce an automatically differentiable version of PySCF, expanding its capabilities for quantum chemistry calculations at and beyond the mean-field level.

Findings

Demonstrated automatic differentiation for orbital optimization and properties

Applied framework to excited-state energies and derivative couplings

Extended capabilities to molecules and solids

Abstract

We introduce an extension to the PySCF package which makes it automatically differentiable. The implementation strategy is discussed, and example applications are presented to demonstrate the automatic differentiation framework for quantum chemistry methodology development. These include orbital optimization, properties, excited-state energies, and derivative couplings, at the mean-field level and beyond, in both molecules and solids. We also discuss some current limitations and directions for future work.