Kinetic Event-Chain Algorithm for Active Matter

TL;DR

This paper introduces a kinetic event-chain Monte-Carlo algorithm tailored for active matter systems, efficiently simulating large-scale self-propelled particle dynamics with high accuracy.

Contribution

The paper presents a novel kinetic event-chain algorithm that accurately and efficiently models active matter, outperforming traditional Brownian dynamics in large-scale simulations.

Findings

Reproduces phase diagram of active disks accurately

Efficiently simulates systems with up to 10^5 particles

Matches physical results from single-particle to many-body effects

Abstract

We present a cluster kinetic Monte-Carlo algorithm for active matter systems of self-propelled particles with special focus on steric interactions. The kinetic event-chain algorithm is based on the event-chain Monte-Carlo method and is applied to active Brownian disks in two dimensions. The algorithm assigns Monte-Carlo moves of active disks a mean time based on a comparison between Brownian dynamics and the dynamics of the event-chain Monte-Carlo method. This time is used to perform diffusional rotation of their propulsion force. We show that the algorithm correctly and efficiently reproduces various physical results ranging from single-particle dynamics to many-body-effects. In particular, we reproduce the phase diagram of active disks and the motility-induced phase separated region with high accuracy. The kinetic event-chain algorithm is shown to be much faster - at comparable accuracy - than (event-driven) Brownian dynamics algorithms, enabling large-scale simulations up to giant systems with $10^5$ particles on standard desktop hardware.