Data-driven molecular modeling with the generalized Langevin equation

TL;DR

This paper introduces a data-driven rational approximation for the generalized Langevin equation to efficiently model complex molecular dynamics, enabling better coarse-grained simulations of intricate molecular systems.

Contribution

It extends previous GLE-based modeling to complex molecules by developing a data-driven approximation that captures memory effects more efficiently.

Findings

Accurately approximates the GLE for complex molecules

Matches exact methods in autocorrelation predictions

Improves simulation efficiency for high-dimensional systems

Abstract

The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these results to more complex molecules, whose many degrees of freedom and complicated dynamics require approximation methods. We demonstrate the effectiveness of our approximation by testing it against exact methods and comparing observables such as autocorrelation and transition rates.