The Monte-Carlo dynamics of a binary Lennard-Jones glass-forming mixture

TL;DR

This study demonstrates that Monte-Carlo simulations efficiently capture the slow dynamics of a binary Lennard-Jones glass-former, aligning well with Newtonian dynamics except at very short times, and reveals dynamics-dependent fluctuations.

Contribution

It introduces Monte-Carlo as a highly efficient method for simulating glassy dynamics and compares its results with other dynamics, highlighting differences in fluctuation behavior.

Findings

Monte-Carlo is 10 times faster than Brownian Dynamics.

Average dynamics agree with Newtonian dynamics at longer times.

Dynamic fluctuations depend on microscopic dynamics.

Abstract

We use a standard Monte-Carlo algorithm to study the slow dynamics of a binary Lennard-Jones glass-forming mixture at low temperature. We find that Monte-Carlo is by far the most efficient way to simulate a stochastic dynamics since relaxation is about 10 times faster than in Brownian Dynamics and about 30 times faster than in Stochastic Dynamics. Moreover, the average dynamical behaviour of the system is in quantitative agreement with the one obtained using Newtonian dynamics, apart at very short times where thermal vibrations are suppressed. We show, however, that dynamic fluctuations quantified by four-point dynamic susceptibilities do retain a dependence on the microscopic dynamics, as recently predicted theoretically.