Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.

ABSTRACT: Coarse-graining (CG) enables molecular dynamics (MD) simulations of larger systems and longer time scales that are otherwise infeasible with atomistic models. Machine learning potentials (MLPs), with their capacity to capture many-body interactions, can provide accurate approximations of the potential of mean force (PMF) in CG models. Current CG MLPs are typically trained in a bottom-up manner via force matching, which in practice relies on configurations sampled from the unbiased equilibrium Boltzmann distribution to ensure thermodynamic consistency. This convention poses two key limitations: first, sufficiently long atomistic trajectories are needed to reach convergence; and second, even once equilibrated, transition regions remain poorly sampled. To address these issues, we employ enhan

SUBMITTER: Chen W 

PROVIDER: S-EPMC12805510 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature