Journal article
Machine learning for a finite size correction in periodic coupled cluster theory calculations
The Journal of chemical physics, Vol.156(20), 204109
05/28/2022
DOI: 10.1063/5.0086580
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 Grüneis for interpolating over the transition structure factor to obtain a finite size correction for CCSD [K. Liao and A. Grüneis, 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 [Mihm et al., 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 Weiler - University of IowaTina N. Mihm - University of IowaJames J. Shepherd - University of Iowa
- Resource Type
- Journal article
- Publication Details
- The Journal of chemical physics, Vol.156(20), 204109
- DOI
- 10.1063/5.0086580
- ISSN
- 0021-9606
- eISSN
- 1089-7690
- Number of pages
- 6
- Grant note
- CHE-2045046 / National Science Foundation (https://doi.org/10.13039/100000001) N/A / University of Iowa (https://doi.org/10.13039/100008893)
- Language
- English
- Date published
- 05/28/2022
- Academic Unit
- Chemistry
- Record Identifier
- 9984259384102771
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