Hard-disk dipoles and non-reversible Markov chains

TL;DR

This paper evaluates various event-chain Monte Carlo algorithms for simulating hard-disk dipoles in two dimensions, demonstrating significant speed advantages over traditional methods and highlighting the effectiveness of Newtonian ECMC in overcoming dynamical arrest.

Contribution

It benchmarks different ECMC algorithms for 2D hard-disk dipoles, revealing their relative efficiencies and suitability for molecular water models.

Findings

ECMC algorithms outperform local reversible Metropolis in speed.

Newtonian ECMC effectively overcomes dynamical arrest.

Significant speed differences observed among ECMC variants.

Abstract

We benchmark event-chain Monte Carlo (ECMC) algorithms for tethered hard-disk dipoles in two dimensions in view of application of ECMC to water models in molecular simulation. We characterize the rotation dynamics of dipoles through the integrated autocorrelation times of the polarization. The non-reversible straight, reflective, forward, and Newtonian ECMC algorithms are all event-driven, and they differ only in their update rules at event times. They realize considerable speedups with respect to the local reversible Metropolis algorithm. We also find significant speed differences among the ECMC variants. Newtonian ECMC appears particularly well-suited for overcoming the dynamical arrest that has plagued straight ECMC for three-dimensional dipolar models with Coulomb interactions.