Operator Forces For Coarse-Grained Molecular Dynamics

TL;DR

This paper introduces flow-based kernels for machine-learned coarse-grained molecular dynamics, reducing local distortions and enabling force field generation from configurational data without force labels.

Contribution

It develops normalizing flow-based kernels that improve force matching accuracy in low-data regimes for coarse-grained simulations.

Findings

Flow-based kernels reduce local distortions compared to noise-based kernels.

High-quality coarse-grained forces can be generated solely from configurational samples.

Method is demonstrated on small proteins with promising results.

Abstract

Coarse-grained (CG) molecular dynamics simulations extend the length and time scale of atomistic simulations by replacing groups of correlated atoms with CG beads. Machine-learned coarse-graining (MLCG) has recently emerged as a promising approach to construct highly accurate force fields for CG molecular dynamics. However, the calibration of MLCG force fields typically hinges on force matching, which demands extensive reference atomistic trajectories with corresponding force labels. In practice, atomistic forces are often not recorded, making traditional force matching infeasible on pre-existing datasets. Recently, noise-based kernels have been introduced to adapt force matching to the low-data regime, including situations in which reference atomistic forces are not present. While this approach produces force fields which recapitulate slow collective motion, it introduces significant local distortions due to the corrupting effects of the noise-based kernel. In this work, we introduce more general kernels based on normalizing flows that substantially reduce these local distortions while preserving global conformational accuracy. We demonstrate our method on small proteins, showing that flow-based kernels can generate high-quality CG forces solely from configurational samples.