Reweighting non-equilibrium steady-state dynamics along collective variables

TL;DR

This paper introduces a MaxCal-based method for reweighting non-equilibrium steady-state dynamics in complex systems using collective variables, enabling analysis of larger systems beyond microtrajectories.

Contribution

It develops a CV-based MaxCal approach that efficiently reweights trajectories in non-equilibrium steady states, extending applicability to larger systems.

Findings

Successfully applied to a 2D particle system and a coarse-grained peptide.

Enables dynamical reweighting across a wide range of driving forces.

Expands the scope of MaxCal methods to higher-dimensional systems.

Abstract

Computer simulations generate microscopic trajectories of complex systems at a single thermodynamic state point. We recently introduced a Maximum Caliber (MaxCal) approach for dynamical reweighting. Our approach mapped these trajectories to a Markovian description on the configurational coordinates, and reweighted path probabilities as a function of external forces. Trajectory probabilities can be dynamically reweighted both from and to equilibrium or non-equilibrium steady states. As the system's dimensionality increases, an exhaustive description of the microtrajectories becomes prohibitive--even with a Markovian assumption. Instead we reduce the dimensionality of the configurational space to collective variables (CVs). Going from configurational to CV space, we define local entropy productions derived from configurationally averaged mean forces. The entropy production is shown to be a suitable constraint on MaxCal for non-equilibrium steady states expressed as a function of CVs. We test the reweighting procedure on two systems: a particle subject to a two-dimensional potential and a coarse-grained peptide. Our CV-based MaxCal approach expands dynamical reweighting to larger systems, for both static and dynamical properties, and across a large range of driving forces.