Resetting Metadynamics

TL;DR

This paper introduces a novel approach combining stochastic resetting with Metadynamics to enhance molecular simulation efficiency, especially when optimal collective variables are unknown or suboptimal, leading to significant speedups and accurate mean first-passage time estimation.

Contribution

It demonstrates that combining stochastic resetting with Metadynamics can outperform individual methods and offers a way to achieve speedups without the need for optimal collective variables.

Findings

Resetting Metadynamics can outperform individual methods in simulation acceleration.

Suboptimal collective variables with resetting can yield speedups comparable to optimal ones.

A new method allows unbiased mean first-passage time extraction from reset simulations.

Abstract

Metadynamics is a powerful method to accelerate molecular dynamics simulations, but its efficiency critically depends on the identification of collective variables that capture the slow modes of the process. Unfortunately, collective variables are usually not known a priori, and finding them can be very challenging. We recently presented a collective variables-free approach to enhanced sampling using stochastic resetting. Here, we combine the two methods for the first time, showing that it can lead to greater acceleration than either of them separately. We also demonstrate that resetting Metadynamics simulations performed with suboptimal collective variables can lead to speedups comparable with those obtained with optimal collective variables. Therefore, the application of stochastic resetting can be an alternative to the challenging task of improving suboptimal collective variables, at almost no additional computational cost. Finally, we propose a method to extract unbiased mean first-passage times from Metadynamics simulations with resetting, resulting in an improved tradeoff between speedup and accuracy. This work opens the way for combining stochastic resetting with other enhanced sampling methods to accelerate a broad range of molecular simulations.