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Machine-learned acceleration for molecular dynamics in CASTEP
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Stenczel, Tamás K., El-Machachi, Zakariya, Liepuoniute, Guoda, Morrow, Joe D., Bartók, Albert P., Probert, Matt I. J., Csányi, Gábor and Deringer, Volker L. (2023) Machine-learned acceleration for molecular dynamics in CASTEP. Journal of Chemical Physics, 159 (4). 044803 . doi:10.1063/5.0155621 ISSN 0021-9606.
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Official URL: https://doi.org/10.1063/5.0155621
Abstract
Machine learning (ML) methods are of rapidly growing interest for materials modeling, and yet, the use of ML interatomic potentials for new systems is often more demanding than that of established density-functional theory (DFT) packages. Here, we describe computational methodology to combine the CASTEP first-principles simulation software with the on-the-fly fitting and evaluation of ML interatomic potential models. Our approach is based on regular checking against DFT reference data, which provides a direct measure of the accuracy of the evolving ML model. We discuss the general framework and the specific solutions implemented, and we present an example application to high-temperature molecular-dynamics simulations of carbon nanostructures. The code is freely available for academic research.
Item Type: | Journal Article | |||||||||||||||||||||||||||||||||
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Subjects: | Q Science > QD Chemistry | |||||||||||||||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Physics |
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SWORD Depositor: | Library Publications Router | |||||||||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Molecular dynamics -- Computer simulation, Machine learning, Density functionals, Atoms, Potential theory (Mathematics), Nuclear forces (Physics) | |||||||||||||||||||||||||||||||||
Journal or Publication Title: | Journal of Chemical Physics | |||||||||||||||||||||||||||||||||
Publisher: | American Institute of Physics | |||||||||||||||||||||||||||||||||
ISSN: | 0021-9606 | |||||||||||||||||||||||||||||||||
Official Date: | 27 July 2023 | |||||||||||||||||||||||||||||||||
Dates: |
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Volume: | 159 | |||||||||||||||||||||||||||||||||
Number: | 4 | |||||||||||||||||||||||||||||||||
Article Number: | 044803 | |||||||||||||||||||||||||||||||||
DOI: | 10.1063/5.0155621 | |||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | ** From Crossref journal articles via Jisc Publications Router ** History: epub 27-07-2023; issued 27-07-2023; ppub 28-07-2023. | |||||||||||||||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 16 August 2023 | |||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 16 August 2023 | |||||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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