A General Algorithm for Sampling Rare Events in Non-Equilibrium and Non-Stationary Systems

TL;DR

This paper introduces a new algorithm for sampling rare events in non-equilibrium, non-stationary systems, capable of handling irreversible dynamics without prior knowledge of reaction pathways.

Contribution

The authors present a general, scalable method for calculating rare event probabilities in complex non-equilibrium systems, validated across multiple models.

Findings

Method accurately estimates rare event probabilities in diverse non-equilibrium models.

Scales well with phase space subdivision, requiring no detailed pathway knowledge.

Validated on Glauber-Ising, Kawasaki-Ising, and p-o ASEP models.

Abstract

Although many computational methods for rare event sampling exist, this type of calculation is not usually practical for general nonequilibrium conditions, with macroscopically irreversible dynamics and away from both stationary and metastable states. A novel method for calculating the time-series of the probability of a rare event is presented which is designed for these conditions. The method is validated for the cases of the Glauber-Ising model under time-varying shear flow, the Kawasaki-Ising model after a quench into the region between nucleation dominated and spinodal decomposition dominated phase change dynamics, and the parallel open asymmetric exclusion process (p-o ASEP). The method requires a subdivision of the phase space of the system: it is benchmarked and found to scale well for increasingly fine subdivisions, meaning that it can be applied without detailed foreknowledge of the physically important reaction pathways.