Accelerating Molecular Dynamics through Informed Resetting

Abstract

We present a procedure for enhanced sampling of molecular dynamics simulations through informed stochastic resetting. Many phenomena, such as protein folding and crystal nucleation, occur over time scales that are inaccessible using standard simulation methods. We recently showed that stochastic resetting can accelerate molecular simulations that exhibit broad transition time distributions. However, standard stochastic resetting does not exploit any information about the reaction progress. Here, we demonstrate that an informed resetting protocol leads to greater accelerations than standard stochastic resetting, both for molecular dynamics and Metadynamics simulations. This is achieved by resetting only when a certain condition is met, e.g., when the distance from the target along the reaction coordinate is larger than some threshold. We then employ recently obtained theoretical results to identify the condition that leads to the greatest acceleration and to infer the unbiased mean transition time from accelerated simulations. Our work significantly extends the applicability of stochastic resetting for enhanced sampling of molecular simulations.