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Trajectory-based machine learning method and its application to molecular dynamics


Han, Ruocheng; Luber, Sandra (2020). Trajectory-based machine learning method and its application to molecular dynamics. Molecular Physics, 118(19-20):e1788189.

Abstract

Ab initio molecular dynamics (AIMD) has become a popular simulation technique but long simulation times are often hampered due to its high computational effort. Alternatively, classical molecular dynamics (MD) based on force fields may be used, which, however, has certain shortcomings compared to AIMD. In order to alleviate that situation, a trajectory-based machine learning (TrajML) approach is introduced for the construction of force fields by learning from AIMD trajectories. Only nuclear trajectories are required, which can be obtained by other methods beyond AIMD as well. We developed an easy-to-use MD machine learning package (TrajML MD) for instant modelling of the force field and system-focussed prediction of molecular configurations for MD trajectories. It consumes similar computational resources as classical MD but can simulate complex systems with a higher accuracy due to the targeted learning on the system of interest.

Abstract

Ab initio molecular dynamics (AIMD) has become a popular simulation technique but long simulation times are often hampered due to its high computational effort. Alternatively, classical molecular dynamics (MD) based on force fields may be used, which, however, has certain shortcomings compared to AIMD. In order to alleviate that situation, a trajectory-based machine learning (TrajML) approach is introduced for the construction of force fields by learning from AIMD trajectories. Only nuclear trajectories are required, which can be obtained by other methods beyond AIMD as well. We developed an easy-to-use MD machine learning package (TrajML MD) for instant modelling of the force field and system-focussed prediction of molecular configurations for MD trajectories. It consumes similar computational resources as classical MD but can simulate complex systems with a higher accuracy due to the targeted learning on the system of interest.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Chemistry
Dewey Decimal Classification:540 Chemistry
Scopus Subject Areas:Life Sciences > Biophysics
Life Sciences > Molecular Biology
Physical Sciences > Condensed Matter Physics
Physical Sciences > Physical and Theoretical Chemistry
Uncontrolled Keywords:Physical and Theoretical Chemistry, Biophysics, Molecular Biology, Condensed Matter Physics
Language:English
Date:17 October 2020
Deposited On:01 Feb 2021 16:04
Last Modified:05 Jul 2021 00:00
Publisher:Taylor & Francis
ISSN:0026-8976
OA Status:Green
Publisher DOI:https://doi.org/10.1080/00268976.2020.1788189
Project Information:
  • : FunderSNSF
  • : Grant IDPP00P2_170667
  • : Project TitleIn Silico Investigation and Design of Bio-inspired Catalysts for Water Splitting

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