Stochastic Resetting for Enhanced Sampling

TL;DR

This paper introduces stochastic resetting as a novel method to significantly accelerate molecular dynamics simulations, enabling the sampling of rare events and long-timescale processes more efficiently.

Contribution

It is the first application of stochastic resetting to molecular simulations, demonstrating acceleration of processes and accurate estimation of transition times from a single restart rate.

Findings

Accelerates molecular processes by up to tenfold.

Recovers mean transition times from accelerated simulations.

Can be combined with other sampling methods for further speed-up.

Abstract

We present a method for enhanced sampling of molecular dynamics simulations using stochastic resetting. Various phenomena, ranging from crystal nucleation to protein folding, occur on timescales that are unreachable in standard simulations. This is often caused by broad transition time distributions in which extremely slow events have a non-negligible probability. Stochastic resetting, i.e., restarting simulations at random times, was recently shown to significantly expedite processes that follow such distributions. Here, we employ resetting for enhanced sampling of molecular simulations for the first time. We show that it accelerates long-timescale processes by up to an order of magnitude in examples ranging from simple models to molecular systems. Most importantly, we recover the mean transition time without resetting - typically too long to be sampled directly - from accelerated simulations at a single restart rate. Stochastic resetting can be used as a standalone method or combined with other sampling algorithms to further accelerate simulations.