Geometric Cluster Algorithm for Interacting Fluids

TL;DR

This paper introduces a geometric cluster algorithm (GCA) for simulating complex fluids, offering a rejection-free, nonlocal approach that improves efficiency especially in size-asymmetric particle systems.

Contribution

The paper presents the GCA as a continuum generalization of spin system cluster algorithms, tailored for fluid simulations with varying particle sizes.

Findings

GCA is effective for size-asymmetric particle systems.

Efficiency of GCA increases with size disparity.

Comparison shows GCA outperforms traditional algorithms in specific fluid models.

Abstract

We discuss a new Monte Carlo algorithm for the simulation of complex fluids. This algorithm employs geometric operations to identify clusters of particles that can be moved in a rejection-free way. It is demonstrated that this geometric cluster algorithm (GCA) constitutes the continuum generalization of the Swendsen-Wang and Wolff cluster algorithms for spin systems. Because of its nonlocal nature, it is particularly well suited for the simulation of fluid systems containing particles of widely varying sizes. The efficiency improvement with respect to conventional simulation algorithms is a rapidly growing function of the size asymmetry between the constituents of the system. We study the cluster-size distribution for a Lennard-Jones fluid as a function of density and temperature and provide a comparison between the generalized GCA and the hard-core GCA for a size-asymmetric mixture with Yukawa-type couplings.