Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics

• URL: http://arxiv.org/abs/2302.07071v1
• Date: Tue, 14 Feb 2023 14:35:39 GMT
• Title: Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
• Authors: Andreas Kr\"amer, Aleksander P. Durumeric, Nicholas E. Charron, Yaoyi Chen, Cecilia Clementi and Frank No\'e
• Abstract summary: coarse-grained (CG) models have the potential to simulate large molecular complexes beyond what is possible with atomistic molecular dynamics. 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.
• Score: 55.41644538483948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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