Contrastive Learning of Coarse-Grained Force Fields.

ABSTRACT: Coarse-grained models have proven helpful for simulating complex systems over long time scales to provide molecular insights into various processes. Methodologies for systematic parametrization of the underlying energy function or force field that describes the interactions among different components of the system are of great interest for ensuring simulation accuracy. We present a new method, potential contrasting, to enable efficient learning of force fields that can accurately reproduce the conformational distribution produced with all-atom simulations. Potential contrasting generalizes the noise contrastive estimation method with umbrella sampling to better learn the complex energy landscape of molecular systems. When applied to the Trp-cage protein, we found that the technique produce

SUBMITTER: Ding X 

PROVIDER: S-EPMC9904862 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature