Preprint
Machine learning for a finite size correction in periodic coupled cluster theory calculations
ArXiv.org
03/31/2022
DOI: 10.48550/arXiv.2204.00092
Abstract
We introduce a straightforward Gaussian process regression (GPR) model for
the transition structure factor of metal periodic coupled cluster singles and
doubles (CCSD) calculations. This is inspired by the method introduced by Liao
and Gr\"uneis for interpolating over the transition structure factor to obtain
a finite size correction for CCSD [J. Chem. Phys. 145, 141102 (2016)], and by
our own prior work using the transition structure factor to efficiently
converge CCSD for metals to the thermodynamic limit [Nat. Comput. Sci. 1, 801
(2021)]. In our CCSD-FS-GPR method to correct for finite size errors, we fit
the structure factor to a 1D function in the momentum transfer, $G$. We then
integrate over this function by projecting it onto a k-point mesh to obtain
comparisons with extrapolated results. Results are shown for lithium, sodium,
and the uniform electron gas.
Details
- Title: Subtitle
- Machine learning for a finite size correction in periodic coupled cluster theory calculations
- Creators
- Laura WeilerTina N MihmJames J Shepherd
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arXiv.2204.00092
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 03/31/2022
- Academic Unit
- Chemistry
- Record Identifier
- 9984240760202771
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