Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces
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. ISSN 0031-9007
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Official URL: http://link.aps.org/doi/10.1103/PhysRevLett.114.09...
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|
|Library of Congress Subject Headings (LCSH):||Molecular dynamics, Machine learning, Quantum theory|
|Journal or Publication Title:||Physical Review Letters|
|Publisher:||American Physical Society|
|Official Date:||6 March 2015|
|Article Number:||Article number 096405|
|Access rights to Published version:||Restricted or Subscription Access|
|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)|
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