Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.

Journal: Journal of chemical theory and computation
Published Date: Jul 16, 2020

Abstract

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.

Authors

  • Paraskevi Gkeka
    Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France.
  • Gabriel Stoltz
    CERMICS, Ecole des Ponts, Marne-la-Vallée, France.
  • Amir Barati Farimani
    Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
  • Zineb Belkacemi
    Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France.
  • Michele Ceriotti
    Laboratory of Computational Science and Modeling (COSMO), École Polytechnique Fédérale de Lausanne Lausanne Switzerland.
  • John D Chodera
    Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.
  • Aaron R Dinner
    Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States.
  • Andrew L Ferguson
    Institute for Molecular Engineering, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, United States.
  • Jean-Bernard Maillet
    CEA-DAM, DIF, 91297 Arpajon Cedex, France.
  • Hervé Minoux
    Integrated Drug Discovery, Sanofi R&D, 94403 Vitry-sur-Seine, France.
  • Christine Peter
    Theoretical Chemistry, University of Konstanz , 78547 Konstanz, Germany.
  • Fabio Pietrucci
    UMR CNRS 7590, MNHN, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, 75005 Paris, France.
  • Ana Silveira
    Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.
  • Alexandre Tkatchenko
  • Zofia Trstanova
    School of Mathematics, The University of Edinburgh, Edinburgh EH9 3FD, U.K.
  • Rafal Wiewiora
    Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.
  • Tony Lelièvre
    CERMICS, Ecole des Ponts, Marne-la-Vallée, France.

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