Event-driven Monte Carlo algorithm for general potentials

TL;DR

This paper introduces an event-driven Monte Carlo algorithm adaptable to general potentials, extending the event-chain method from hard spheres to broader systems, with improved efficiency and flexibility.

Contribution

It presents a novel extension of the event-chain Monte Carlo algorithm to general potentials in the micro-canonical ensemble, allowing for non-local, rejection-free sampling.

Findings

Algorithm is asymptotically independent of potential discretization

Implemented for cut-off linear potential

Potential for direct continuum limit implementation

Abstract

We extend the event-chain Monte Carlo algorithm from hard-sphere interactions to the micro-canonical ensemble (constant potential energy) for general potentials. This event-driven Monte Carlo algorithm is non-local, rejection-free, and allows for the breaking of detailed balance. The algorithm uses a discretized potential, but its running speed is asymptotically independent of the discretization. We implement the algorithm for the cut-off linear potential, and discuss its possible implementation directly in the continuum limit.