Dataset for: Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data

William Z Van Benschoten; Laura Weiler; Gabriel J Smith; Songhang Man; Taylor DeMello; James J Shepherd

doi:10.25820/data.006428

Back

Dataset

Open access

Dataset for: Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data

William Z Van Benschoten, Laura Weiler, Gabriel J Smith, Songhang Man, Taylor DeMello and James J Shepherd

University of Iowa

12/16/2024

DOI: 10.25820/data.006428

Files and links (4)

txt

Readme7.40 kBDownload View

READMEDataset information: creators, funding sources, abstract, file information, methods.Open Data Commons Attribution (ODC-By) V1.0, Open Access

txt

DataDictionary_VanBenschoten202316.19 kBDownload View

Data DictionaryA table listing the data elements/variable names/column headings, with descriptions, alternate names, data types, character lengths, acceptable values, and whether or not values are required and if nulls are accepted entries.Open Data Commons Attribution (ODC-By) V1.0, Open Access

txt

DataStructure_VanBenschoten202353.44 kBDownload View

File list and directory structure (for zipped files) with brief descriptions of all data files.Open Data Commons Attribution (ODC-By) V1.0, Open Access

url

Link to Globus for downloading the data filesView

Data files and documentation are accessible via this link to Globus. Download from Globus via web browser or Globus app.Open Data Commons Attribution (ODC-By) V1.0

Abstract

This dataset contains finite temperature specific heat capacities and entropies calculated using Gaussian process regression (GPR) applied to finite temperature density matrix quantum Monte Carlo data for several molecular benchmark systems and two systems beyond which exact data can be calculated. Additionally, for comparison, numerical and spline methods were used to approximate the specific heat and entropy similar to the GPR methodology. Finally, there is exact and approximate methods data for the same quantities, calculated using analytical expressions. This data was collected and discussed in the manuscript: "Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data", for more information see: https://doi.org/10.1063/5.0150702.

Gaussian process regression

Heat entropies

Molecular benchmarks

Noisy data

Monte Carlo data

Details

Title: Subtitle: Dataset for: Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data
Creators: William Z Van Benschoten - University of Iowa
Laura Weiler - University of Iowa
Gabriel J Smith - University of Iowa
Songhang Man - University of Iowa
Taylor DeMello - University of Iowa
James J Shepherd - University of Iowa, Chemistry
Contributors: Brian Westra (Data Curator) - University of Iowa, Humanities and Social Sciences/Scholarly Impact
Resource Type: Dataset
Publisher: University of Iowa
DOI: 10.25820/data.006428
Grants: Early Career Research Program (ECRP), DE-SC0021317, United States Department of Energy (United States, Washington) - DOE
High temperature electronic structure theory using density matrix quantum Monte Carlo, ERCAP0019952, National Energy Research Scientific Computing Center (United States, Berkeley) - NERSC
Language: English
Date collected: 01/2023
Date published: 12/16/2024
Academic Unit: Chemistry; Humanities and Social Sciences/Scholarly Impact
Record Identifier: 9984401360102771

Metrics

1 Record Views

Dataset for: Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data

Files and links (4)

Related content

Abstract

Details

Metrics