Published November 28, 2022 | Version public
Journal Article

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

  • 1. ROR icon California Institute of Technology

Abstract

We introduce an extension to the PʏSCF 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.

Additional Information

This work was supported by the US Department of Energy through the US DOE, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division, under Triad National Security, LLC ("Triad") contract Grant No. 89233218CNA000001. Support for the PySCF ML infrastructure on top of which PySCFAD was built comes from the Dreyfus Foundation.

Additional details

Identifiers

Eprint ID
119234
Resolver ID
CaltechAUTHORS:20230213-465520400.2

Funding

Department of Energy (DOE)
89233218CNA000001
Camille and Henry Dreyfus Foundation

Dates

Created
2023-03-24
Created from EPrint's datestamp field
Updated
2023-03-24
Created from EPrint's last_modified field