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Statistically optimal force aggregation for coarse-graining molecular dynamics

  • 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 bottom-up 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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Metadaten
Author:Andreas Krämer, Aleksander Durumeric, Nicholas Charron, Yaoyi Chen, Cecilia Clementi, Frank Noé
Document Type:Article
Parent Title (English):The Journal of Physical Chemistry Letters
Volume:14
Issue:17
First Page:3970
Last Page:3979
Year of first publication:2023
DOI:https://doi.org/10.1021/acs.jpclett.3c00444
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