Inference of non-exponential kinetics through stochastic resetting

TL;DR

This paper introduces a novel inference scheme for long-timescale, non-exponential kinetics using stochastic resetting in molecular dynamics, enabling accurate estimation of kinetics beyond traditional simulation timescales.

Contribution

The authors develop a new inference method tailored for non-exponential first-passage time distributions, overcoming limitations of existing exponential-based schemes.

Findings

Successfully estimates mean first-passage times in model and peptide systems.

Accelerates sampling by over an order of magnitude.

Provides accurate long-time asymptotics for non-exponential kinetics.

Abstract

We present an inference scheme of long timescale, non-exponential kinetics from Molecular Dynamics simulations accelerated by stochastic resetting. Standard simulations provide valuable insight into chemical processes but are limited to timescales shorter than $\sim 1 \mu s$. Slower processes require the use of enhanced sampling methods to expedite them, and inference schemes to obtain the unbiased kinetics. However, most kinetics inference schemes assume an underlying exponential first-passage time distribution and are inappropriate for other distributions, e.g., with a power-law decay. We propose an inference scheme that is designed for such cases, based on simulations enhanced by stochastic resetting. We show that resetting promotes enhanced sampling of the first-passage time distribution at short timescales, but often also provides sufficient information to estimate the long-time asymptotics, which allows the kinetics inference. We apply our method to a model system and a short peptide in an explicit solvent, successfully estimating the unbiased mean first-passage time while accelerating the sampling by more than an order of magnitude.