An Infinite Swapping Approach to the Rare-Event Sampling Problem

TL;DR

This paper introduces an infinite swapping method for rare-event Monte Carlo sampling, using symmetrization to improve sampling efficiency in complex probability distributions, demonstrated through numerical experiments on Lennard-Jones clusters.

Contribution

The paper presents a novel infinite swapping approach that enhances rare-event sampling by symmetrizing probability distributions, improving connectivity and sampling efficiency.

Findings

Effective sampling of rare events in Lennard-Jones clusters

Symmetrization increases probability distribution connectivity

Method improves efficiency over traditional sampling techniques

Abstract

We describe a new approach to the rare-event Monte Carlo sampling problem. This technique utilizes a symmetrization strategy to create probability distributions that are more highly connected and thus more easily sampled than their original, potentially sparse counterparts. After discussing the formal outline of the approach and devising techniques for its practical implementation, we illustrate the utility of the technique with a series of numerical applications to Lennard-Jones clusters of varying complexity and rare-event character.