The Library
Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces
Tools
Li, Zhenwei, Kermode, James R. and De Vita, Alessandro (2015) Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces. Physical Review Letters, Volume 114 . Article number 096405. doi:10.1103/PhysRevLett.114.096405 ISSN 0031-9007.
|
PDF
WRAP_PhysRevLett.114.096405.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution. Download (494Kb) | Preview |
Official URL: http://link.aps.org/doi/10.1103/PhysRevLett.114.09...
Abstract
We present a molecular dynamics scheme which combines first-principles and machine-learning (ML) techniques in a single information-efficient approach. Forces on atoms are either predicted by Bayesian inference or, if necessary, computed by on-the-fly quantum-mechanical (QM) calculations and added to a growing ML database, whose completeness is, thus, never required. As a result, the scheme is accurate and general, while progressively fewer QM calls are needed when a new chemical process is encountered for the second and subsequent times, as demonstrated by tests on crystalline and molten silicon.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Subjects: | Q Science > QC Physics | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
Library of Congress Subject Headings (LCSH): | Molecular dynamics, Machine learning, Quantum theory | ||||||
Journal or Publication Title: | Physical Review Letters | ||||||
Publisher: | American Physical Society | ||||||
ISSN: | 0031-9007 | ||||||
Official Date: | 6 March 2015 | ||||||
Dates: |
|
||||||
Volume: | Volume 114 | ||||||
Article Number: | Article number 096405 | ||||||
DOI: | 10.1103/PhysRevLett.114.096405 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 29 December 2015 | ||||||
Date of first compliant Open Access: | 29 December 2015 | ||||||
Funder: | Imperial College, London. Rio Tinto Centre for Advanced Mineral Recovery, Seventh Framework Programme (European Commission) (FP7), Engineering and Physical Sciences Research Council (EPSRC), King's College London, United States. Department of Energy. Office of Science | ||||||
Grant number: | EP/L014742/1 (EPSRC), EP/L027682/1 (EPSRC), DE-AC02-06CH11357 (DOE) |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year