Published November 28, 2022
| public
Journal Article
Differentiable quantum chemistry with PʏSCF for molecules and materials at the mean-field level and beyond
- Creators
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Zhang, Xing
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Chan, Garnet Kin-Lic
Chicago
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
- Eprint ID
- 119234
- Resolver ID
- CaltechAUTHORS:20230213-465520400.2
- Department of Energy (DOE)
- 89233218CNA000001
- Camille and Henry Dreyfus Foundation
- Created
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2023-03-24Created from EPrint's datestamp field
- Updated
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2023-03-24Created from EPrint's last_modified field