The Library
Gaussian Approximation Potentials : theory, software implementation and application examples
Tools
Klawohn, Sascha, Darby, James P., Kermode, James R., Csányi, Gábor, Caro, Miguel A. and Bartók, Albert P. (2023) Gaussian Approximation Potentials : theory, software implementation and application examples. Journal of Chemical Physics, 159 . 174108. doi:10.1063/5.0160898 ISSN 0021-9606.
|
PDF
WRAP-Gaussian-Approximation-Potentials-theory-software-implementation-application-examples-2023.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (5Mb) | Preview |
|
PDF
WRAP-Gaussian-Approximation-Potentials-theory-software-implementation-application-examples-2023.pdf - Unspecified Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (1825Kb) |
Official URL: https://doi.org/10.1063/5.0160898
Abstract
Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models using ab initio data and using the resulting potentials in atomic simulations. Details of the GAP theory, algorithms and software are presented, together with detailed usage examples to help new and existing users. We review some recent developments to the GAP framework, including Message Passing Interface parallelisation of the fitting code enabling its use on thousands of central processing unit cores and compression of descriptors to eliminate the poor scaling with the number of different chemical elements.
Item Type: | Journal Article | ||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Alternative Title: | |||||||||||||||||||||||||
Subjects: | Q Science > QA Mathematics Q Science > QC Physics |
||||||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Physics |
||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Gaussian processes, Machine learning -- Mathematical models, Distribution (Probability theory) | ||||||||||||||||||||||||
Journal or Publication Title: | Journal of Chemical Physics | ||||||||||||||||||||||||
Publisher: | American Institute of Physics | ||||||||||||||||||||||||
ISSN: | 0021-9606 | ||||||||||||||||||||||||
Official Date: | 6 November 2023 | ||||||||||||||||||||||||
Dates: |
|
||||||||||||||||||||||||
Volume: | 159 | ||||||||||||||||||||||||
Article Number: | 174108 | ||||||||||||||||||||||||
DOI: | 10.1063/5.0160898 | ||||||||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||||||||
Date of first compliant deposit: | 8 November 2023 | ||||||||||||||||||||||||
Date of first compliant Open Access: | 8 November 2023 | ||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
|
||||||||||||||||||||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year