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Machine Learning of Coarse-Grained Molecular Dynamics Force Fields.


ABSTRACT: 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.

SUBMITTER: Wang J 

PROVIDER: S-EPMC6535777 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Machine Learning of Coarse-Grained Molecular Dynamics Force Fields.

Wang Jiang J   Olsson Simon S   Wehmeyer Christoph C   Pérez Adrià A   Charron Nicholas E NE   de Fabritiis Gianni G   Noé Frank F   Clementi Cecilia C  

ACS central science 20190415 5


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 reformu  ...[more]

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