Dataset for "Machine learning for a finite size correction in periodic coupled cluster theory calculations"

Laura Weiler; Tina N Mihm; James J Shepherd

doi:10.25820/data.006175

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Dataset for "Machine learning for a finite size correction in periodic coupled cluster theory calculations"

Laura Weiler, Tina N Mihm and James J Shepherd

University of Iowa

03/02/2023

DOI: 10.25820/data.006175

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DATASET_ML1_Weiler2022_README5.27 kBDownload View

READMEOverview of the data, with abstract, creators, funding, data sources, methods, and file information.Open Data Commons Attribution (ODC-By) V1.0, Open Access

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DATASTRUCTURE_ML1_Weiler2022_README1.87 kBDownload View

READMEList of the organization of the files in the zipped folders. Open Data Commons Attribution (ODC-By) V1.0, Open Access

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DATADICTIONARY_ML1_Weiler20226.51 kBDownload View

Data DictionaryOpen Data Commons Attribution (ODC-By) V1.0, Open Access

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Li43.34 kBDownload View

Open Data Commons Attribution (ODC-By) V1.0, Open Access

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Na51.51 kBDownload View

Open Data Commons Attribution (ODC-By) V1.0, Open Access

Abstract

This data set contains input and output files used for generating CCSD-FS-GPR energy predictions for the uniform electron gas at rs=5 (density parameter), and the bcc phase of lithium and sodium metals. The input data includes the transition structure factors used to train Gaussian process regression (GPR) models, as well as the electron repulsion integrals, momentum transfer vector magnitudes, and momentum transfer counts needed to calculate coupled cluster singles and doubles (CCSD) correlation energies. Output files include GPR predicted structure factors with the corresponding momentum transfer vector magnitudes on both discrete and continuous domains. These outputs also contain energy predictions calculated using the GPR-predicted structure factors. Energy predictions extrapolated to zero on a 1/N domain (where N is electron number) or a 1/k^3 domain (where k refers to the k-points used for real systems). These extrapolations are referred to as CCSD-FS-GPR. CCSD and finite-size corrected CCSD (CCSD-FS) are provided as our benchmarks for comparison.

Details

Title: Subtitle: Dataset for "Machine learning for a finite size correction in periodic coupled cluster theory calculations"
Creators: Laura Weiler - University of Iowa
Tina N Mihm
James J Shepherd - University of Iowa, Chemistry
Resource Type: Dataset
Publisher: University of Iowa
DOI: 10.25820/data.006175
Grants: CAREER: Developing Wavefunction-based Quantum Chemistry for Solids, 2045046, National Science Foundation (United States, Alexandria) - NSF
Language: English
Date published: 03/02/2023
Academic Unit: Chemistry
Record Identifier: 9984240031102771

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Dataset for "Machine learning for a finite size correction in periodic coupled cluster theory calculations"

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