Reweighted Jarzynski sampling: Acceleration of rare events and free energy calculation with a bias potential learned from nonequilibrium work

TL;DR

This paper presents a new enhanced sampling method that uses nonequilibrium work data and neural networks to efficiently compute free energy differences and barriers in molecular systems.

Contribution

It introduces a reweighted Jarzynski sampling approach that learns a bias potential from nonequilibrium work to accelerate rare event sampling and improve free energy estimates.

Findings

Achieves rapid convergence of free energy calculations.

Effective across diverse chemical and physical processes.

Uses neural networks for compact free energy representation.

Abstract

We introduce a simple enhanced sampling approach for the calculation of free energy differences and barriers along a one-dimensional reaction coordinate. First, a small number of short nonequilibrium simulations are carried out along the reaction coordinate, and the Jarzynski equality is used to learn an approximate free energy surface from the nonequilibrium work distribution. This free energy estimate is represented in a compact form as an artificial neural network and used as an external bias potential to accelerate rare events in a subsequent molecular dynamics simulation. The final free energy estimate is then obtained by reweighting the equilibrium probability distribution of the reaction coordinate sampled under the influence of the external bias. We apply our reweighted Jarzynski sampling recipe to four processes of varying scales and complexities-spanning chemical reaction in the gas phase, pair association in solution, and droplet nucleation in supersaturated vapor. In all cases, we find reweighted Jarzynski sampling to be a very efficient strategy, resulting in rapid convergence of the free energy to high precision.