The Library
Optimal data generation for machine learned interatomic potentials
Tools
Allen, Connor and Bartók, Albert P. (2022) Optimal data generation for machine learned interatomic potentials. Machine Learning : Science and Technology, 3 (4). 045031. doi:10.1088/2632-2153/ac9ae7 ISSN 2632-2153.
|
PDF
WRAP-Optimal-data-generation-for-machine-learned-interatomic-potentials-Bartok-2022.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (999Kb) | Preview |
Official URL: https://doi.org/10.1088/2632-2153/ac9ae7
Abstract
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human effort. A computationally efficient method is presented to generate databases of atomic configurations that contain optimal information on the small-displacement regime of the potential energy surface of bulk crystalline matter. Utilising non-diagonal supercell (NDSC)1, an automatic process is suggested for ab initio data generation. MLIPs were fitted for Al, W, Mg and Si, which very closely reproduce the ab initio phonon and elastic properties. The protocol can be easily adapted to other materials and can be inserted in the workflow of any flavour of MLIP generation.
Item Type: | Journal Article | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QC Physics Q Science > QD Chemistry |
||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Science > Physics |
||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Atoms, Molecular dynamics -- Computer simulation, Many-body problem, Nuclear forces (Physics), Density functionals -- Research, Mathematical physics | ||||||||||||||||||
Journal or Publication Title: | Machine Learning : Science and Technology | ||||||||||||||||||
Publisher: | IOP Publishing Ltd | ||||||||||||||||||
ISSN: | 2632-2153 | ||||||||||||||||||
Official Date: | 28 December 2022 | ||||||||||||||||||
Dates: |
|
||||||||||||||||||
Volume: | 3 | ||||||||||||||||||
Number: | 4 | ||||||||||||||||||
Article Number: | 045031 | ||||||||||||||||||
DOI: | 10.1088/2632-2153/ac9ae7 | ||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||
Copyright Holders: | UK Ministry of Defence | ||||||||||||||||||
Date of first compliant deposit: | 13 September 2022 | ||||||||||||||||||
Date of first compliant Open Access: | 14 September 2022 | ||||||||||||||||||
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