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ACS Cent Sci. 2019 May 22;5(5):755-767. doi: 10.1021/acscentsci.8b00913. Epub 2019 Apr 15.

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields.

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Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.
Department of Chemistry, Rice University, Houston, Texas 77005, United States.
Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195 Berlin, Germany.
Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/Dr Aiguader 88, 08003 Barcelona, Spain.
Department of Physics, Rice University, Houston, Texas 77005, United States.
Institucio Catalana de Recerca i Estudis Avanats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain.


Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.

Conflict of interest statement

The authors declare no competing financial interest.

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