Efficient Rare Event Simulation by Optimal Nonequilibrium Forcing

TL;DR

This paper introduces an efficient method for rare event simulation in molecular systems by optimizing nonequilibrium forcing through a cross-entropy minimization approach, improving sampling efficiency.

Contribution

It proposes a novel optimization-based approach to find the best nonequilibrium forcing, replacing path sampling with a more efficient iterative minimization strategy.

Findings

The method accelerates rare event sampling in equilibrium systems.

It demonstrates improved efficiency over traditional path sampling techniques.

The approach is validated through numerical experiments.

Abstract

Rare event simulation and estimation for systems in equilibrium are among the most challenging topics in molecular dynamics. As was shown by Jarzynski and others, nonequilibrium forcing can theoretically be used to obtain equilibrium rare event statistics. The advantage seems to be that the external force can speed up the sampling of the rare events by biasing the equilibrium distribution towards a distribution under which the rare events is no longer rare. Yet algorithmic methods based on Jarzynski's and related results often fail to be efficient because they are based on sampling in path space. We present a new method that replaces the path sampling problem by minimization of a cross-entropy-like functional which boils down to finding the optimal nonequilibrium forcing. We show how to solve the related optimization problem in an efficient way by using an iterative strategy based on milestoning.