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A framework for machine‐learning‐augmented multiscale atomistic simulations on parallel supercomputers
Author(s) -
Caccin Marco,
Li Zhenwei,
Kermode James R.,
De Vita Alessandro
Publication year - 2015
Publication title -
international journal of quantum chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 105
eISSN - 1097-461X
pISSN - 0020-7608
DOI - 10.1002/qua.24952
Subject(s) - massively parallel , molecular dynamics , scaling , computer science , quantum , partition (number theory) , computational science , statistical physics , parallel computing , physics , chemistry , computational chemistry , quantum mechanics , mathematics , geometry , combinatorics
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. © 2015 Wiley Periodicals, Inc.