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

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• Macroscopically constrained Wang-Landau method for systems with multiple order parameters and its application to drawing complex phase diagrams
• Approximate ground states of the random-field Potts model from graph cuts
• A brief history of the introduction of generalized ensembles to Markov chain Monte Carlo simulations
• Extension of the constant exchange probability method to multi-dimensional replica exchange Monte Carlo applied to the tri-critical spin-1 Blume-Capel model