Towards an optimal flow: Density-of-states-informed replica-exchange simulations

TL;DR

This paper introduces a density-of-states-informed replica-exchange (g-RE) method that enhances convergence by actively guiding the simulation towards equilibrium and adaptively optimizing temperature sets, significantly improving sampling efficiency.

Contribution

The paper presents a novel g-RE scheme that uses density-of-states information to improve replica-exchange simulations through resampling and adaptive temperature selection.

Findings

Accelerates convergence of RE simulations

Improves sampling around phase transitions

Optimizes temperature flow in simulations

Abstract

Replica exchange (RE) is one of the most popular enhanced-sampling simulations technique in use today. Despite widespread successes, RE simulations can sometimes fail to converge in practical amounts of time, e.g., when sampling around phase transitions, or when a few hard-to-find configurations dominate the statistical averages. We introduce a generalized RE scheme, density-of-states-informed RE (g-RE), that addresses some of these challenges. The key feature of our approach is to inform the simulation with readily available, but commonly unused, information on the the density of states of the system as the RE simulation proceeds. This enables two improvements, namely, the introduction of resampling moves that actively move the system towards equilibrium, and the continual adaptation of the optimal temperature set. As a consequence of these two innovations, we show that the configuration flow in temperature space is optimized and that the overall convergence of RE simulations can be dramatically accelerated.