Krämer, Andreas and Durumeric, Alexander E.P. and Charron, Nicholas E. and Chen, Yaoyi and Clementi, Cecilia and Noé, Frank
(2023)
*Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics.*
The Journal of Physical Chemistry, 14
.
pp. 3970-3979.

Full text not available from this repository.

Official URL: https://doi.org/10.1021/acs.jpclett.3c00444

## Abstract

Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We show that there is flexibility in how to map all-atom forces to the CG representation, and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force-fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins Chignolin and Tryptophan Cage and published as open-source code.

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences Mathematical and Computer Sciences > Mathematics Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics |

ID Code: | 2953 |

Deposited By: | Monika Drueck |

Deposited On: | 20 Apr 2023 08:17 |

Last Modified: | 01 Feb 2024 12:07 |

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