Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics

Autor:	Krämer, Andreas, Durumeric, Aleksander P., Charron, Nicholas E., Chen, Yaoyi, Clementi, Cecilia, Noé, Frank
Rok vydání:	2023
Předmět:	Physics - Chemical Physics Physics - Biological Physics Physics - Computational Physics Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 44 pages, 19 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.07071 Zobrazit plný text záznamu View this record from Arxiv