Wang, J. and Olsson, S. and Wehmeyer, C. and Perez, A. and Charron, N.E. and de Fabritiis, G. and Noé, F. and Clementi, C.
(2019)
*Machine Learning of coarse-grained Molecular Dynamics Force Fields.*
ACS Cent. Sci., 5
(5).
pp. 755-767.
ISSN 2374-7943, ESSN: 2374-7951

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Official URL: https://dx.doi.org/10.1021/acscentsci.8b00913

## 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 multi-body terms that emerge from the dimensionality reduction.

Item Type: | Article |
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Additional Information: | SFB1114 Preprint in arXiv:1812.01736 |

Subjects: | Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Molecular Biology |

ID Code: | 2351 |

Deposited By: | Silvia Hoemke |

Deposited On: | 25 Jun 2019 11:54 |

Last Modified: | 25 Jun 2019 11:59 |

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