Repository: Freie Universität Berlin, Math Department

Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics

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.

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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
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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