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A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers
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Caccin, Marco, Li, Zhenwei, Kermode, James R. and De Vita, Alessandro (2016) A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers. International Journal of Quantum Chemistry, 115 (16). pp. 1129-1139. doi:10.1002/qua.24952 ISSN 0020-7608.
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Official URL: http://dx.doi.org/10.1002/qua.24952
Abstract
Recent advances in quantum mechanical (QM)-based molecular dynamics (MD) simulations have used machine-learning (ML) to predict, rather than recalculate, QM-accurate forces in atomic configurations sufficiently similar to previously encountered ones. Here, we discuss how ML approaches can be deployed within large-scale QM/MM materials simulations on massively parallel supercomputers, making QM zones of ≳1000 atoms routinely attainable. We argue that the ML approach allows computational effort to be concentrated on the most chemically active subregions of the QM zone, significantly improving the overall efficiency of the simulation. We thus propose a novel method to partition large QM regions into multiple subregions, which can be computed in parallel to achieve optimal scaling. Then we review a recently proposed QM/ML MD scheme (Z. Li, J.R. Kermode, A. De Vita Phys. Rev. Lett., 2015, 114, 096405), discussing how this could be efficiently combined with QM-zone partitioning.
Item Type: | Journal Article | ||||||||||
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Subjects: | Q Science > QC Physics | ||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||||
Library of Congress Subject Headings (LCSH): | Machine learning, Quantum theory , High performance computing—Research, Parallel processing (Electronic computers) | ||||||||||
Journal or Publication Title: | International Journal of Quantum Chemistry | ||||||||||
Publisher: | John Wiley & Sons | ||||||||||
ISSN: | 0020-7608 | ||||||||||
Official Date: | 15 August 2016 | ||||||||||
Dates: |
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Volume: | 115 | ||||||||||
Number: | 16 | ||||||||||
Page Range: | pp. 1129-1139 | ||||||||||
DOI: | 10.1002/qua.24952 | ||||||||||
Status: | Peer Reviewed | ||||||||||
Publication Status: | Published | ||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||
Date of first compliant deposit: | 22 December 2015 | ||||||||||
Date of first compliant Open Access: | 27 June 2016 | ||||||||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC), Imperial College, London. Rio Tinto Centre for Advanced Mineral Recovery, United States. Department of Energy. Office of Science | ||||||||||
Grant number: | EP/L014742/1, EP/L027682/1, DE-AC02-06CH11357 |
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