Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems
Year: 2020
Type: review
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, Gabriel Stoltz, Amir Barati Farimani, Zineb Belkacemi, Michele Ceriotti, John D. Chodera, Aaron R. Dinner, Andrew L. Ferguson, Jean-Bernard Maillet, Hervé Minoux, Christine Peter, Fabio Pietrucci, Ana Silveira, Alexandre Tkatchenko, Zofia Trstanova, Rafal Wiewiora, Tony Lelièvre
Institutions: Sanofi (France), Institut national de recherche en sciences et technologies du numérique, École nationale des ponts et chaussées, CERMICS, Carnegie Mellon University, École Polytechnique Fédérale de Lausanne, Memorial Sloan Kettering Cancer Center, University of Chicago, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA DAM Île-de-France, University of Konstanz, Centre National de la Recherche Scientifique, Sorbonne Université, Institut de minéralogie, de physique des matériaux et de cosmochimie, University of Luxembourg, University of Edinburgh
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3016366862