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Gaussian process regression for materials and molecules
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Deringer, Volker L., Bartók, Albert P., Bernstein, Noam, Wilkins, David M., Ceriotti, Michele and Csányi, Gábor (2021) Gaussian process regression for materials and molecules. Chemical Reviews, 121 (16). pp. 10073-10141. doi:10.1021/acs.chemrev.1c00022 ISSN 0009-2665.
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WRAP-Gaussian-process-regression-materials-molecules-Bartók-2021.pdf - Accepted Version - Requires a PDF viewer. Download (13Mb) | Preview |
Official URL: https://doi.org/10.1021/acs.chemrev.1c00022
Abstract
We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation and regression, as well as the question how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.
Item Type: | Journal Article | ||||||||||||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QC Physics Q Science > QD Chemistry T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Computational chemistry -- Data processing, Materials science -- Data processing, Condensed matter -- Data processing, Gaussian processes , Density functionals , Machine learning | ||||||||||||||||||
Journal or Publication Title: | Chemical Reviews | ||||||||||||||||||
Publisher: | American Chemical Society | ||||||||||||||||||
ISSN: | 0009-2665 | ||||||||||||||||||
Official Date: | 25 August 2021 | ||||||||||||||||||
Dates: |
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Volume: | 121 | ||||||||||||||||||
Number: | 16 | ||||||||||||||||||
Page Range: | pp. 10073-10141 | ||||||||||||||||||
DOI: | 10.1021/acs.chemrev.1c00022 | ||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||
Reuse Statement (publisher, data, author rights): | “This document is the Accepted Manuscript version of a Published Work that appeared in final form in Chemical Reviews, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [insert ACS Articles on Request author-directed link to Published Work, see http://pubs.acs.org/page/policy/articlesonrequest/index.html].” | ||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||
Date of first compliant deposit: | 20 July 2021 | ||||||||||||||||||
Date of first compliant Open Access: | 16 August 2022 | ||||||||||||||||||
RIOXX Funder/Project Grant: |
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