ArticleThe journal of physical chemistry letters2022
Stochastic Resetting for Enhanced Sampling.
Article in The journal of physical chemistry letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Adaptive resetting for informed search strategies and the design of non-equilibrium steady-states.Nature communications · 2025Article
- Nonequilibrium Self-Assembly Control by the Stochastic Landscape Method.Journal of chemical information and modeling · 2025Article
- Accelerating Molecular Dynamics through Informed Resetting.Journal of chemical theory and computation · 2025Article
- Short-Time Infrequent Metadynamics for Improved Kinetics Inference.Journal of chemical theory and computation · 2024Article
- Combining stochastic resetting with Metadynamics to speed-up molecular dynamics simulations.Nature communications · 2024Article
- Stochastic Resetting for Enhanced Sampling.The journal of physical chemistry letters · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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