Data-driven Langevin modeling of nonequilibrium processes

TL;DR

This paper introduces an extension of the data-driven Langevin equation approach to model nonequilibrium processes from molecular dynamics data, capturing complex dynamics under external driving and finite sampling effects.

Contribution

It develops an efficient method to construct multidimensional Langevin models for nonequilibrium systems directly from data, expanding the applicability of previous equilibrium-focused models.

Findings

Successfully models sodium chloride dissociation in water.

Accurately captures pressure-jump induced nucleation.

Replicates conformational dynamics of a helical peptide.

Abstract

Given nonstationary data from molecular dynamics simulations, a Markovian Langevin model is constructed that aims to reproduce the time evolution of the underlying process. While at equilibrium the free energy landscape is sampled, nonequilibrium processes can be associated with a biased energy landscape, which accounts for finite sampling effects and external driving. Extending the data-driven Langevin equation (dLE) approach [Phys.\ Rev.\ Lett.\ {\bf 115}, 050602 (2015)] to the modeling of nonequilibrium processes, an efficient way to calculate multidimensional Langevin fields is outlined. The dLE is shown to correctly account for various nonequilibrium processes, including the enforced dissociation of sodium chloride in water, the pressure-jump induced nucleation of a liquid of hard spheres, and the conformational dynamics of a helical peptide sampled from nonstationary short trajectories.