Stochastic Resetting for Enhanced Sampling

Ofir Blumer, Shlomi Reuveni, Barak Hirshberg*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 time scales that are unreachable in standard simulations. They are often characterized 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 time scale processes by up to an order of magnitude in examples ranging from simple models to a molecular system. Most importantly, we recover the mean transition time without resetting, which is 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.

Original languageEnglish
Pages (from-to)11230-11236
Number of pages7
JournalJournal of Physical Chemistry Letters
Volume13
Issue number48
DOIs
StatePublished - 8 Dec 2022

Funding

FundersFunder number
Horizon 2020 Framework Programme
European Research Council
United States-Israel Binational Science Foundation2020083
Israel Science Foundation394/19, 1037/22, 1312/22
Horizon 2020947731

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