Optimized ensemble Monte Carlo simulations of dense Lennard-Jones fluids

TL;DR

This paper introduces an adaptive ensemble Monte Carlo method to efficiently simulate dense Lennard-Jones fluids, significantly improving equilibration and accuracy in capturing interstitial states and structural properties.

Contribution

The authors develop and apply an adaptive ensemble optimization technique that enhances sampling efficiency and accuracy in dense fluid simulations using broad-histogram Monte Carlo methods.

Findings

Improved equilibration by sampling optimized histograms in radial coordinates.

Accurate identification of interstitial states near entropic barriers.

High-precision calculations of radial distribution functions and potentials.

Abstract

We apply the recently developed adaptive ensemble optimization technique to simulate dense Lennard-Jones fluids and a particle-solvent model by broad-histogram Monte Carlo techniques. Equilibration of the simulated fluid is improved by sampling an optimized histogram in radial coordinates that shifts statistical weight towards the entropic barriers between the shells of the liquid. Interstitial states in the vicinity of these barriers are identified with unprecedented accuracy by sharp signatures in the quickly converging histogram and measurements of the local diffusivity. The radial distribution function and potential of mean force are calculated to high precision.