Stochastic Resetting for Enhanced Sampling

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.

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Cited by in corpus (12)

• Stochastic Resetting for Enhanced Sampling
• Thermodynamic work of partial resetting
• Instability in the quantum restart problem
• Stochastic resetting in discrete-time quantum dynamics: steady states and correlations in few-qubit systems
• Queues with resetting: a perspective
• Stochastic resetting prevails over sharp restart for broad target distributions
• Dynamically emergent correlations in Brownian particles subject to simultaneous non-Poissonian resetting protocols
• Random resetting in search problems
• Stochastic Resetting Mitigates Latent Gradient Bias of SGD from Label Noise
• First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
• Chaotic resetting: A resetting strategy for deterministic chaotic systems
• Universal Linear Response of First-Passage Kinetics: A Framework for Prediction and Inference