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.
@article{arxiv.2207.13836,
title = {Differentiable quantum chemistry with PySCF for molecules and materials at the mean-field level and beyond},
author = {Xing Zhang and Garnet Kin-Lic Chan},
journal= {arXiv preprint arXiv:2207.13836},
year = {2024}
}