The G-JF Thermostat for Accurate Configurational Sampling in Soft-Matter Simulations

TL;DR

This paper introduces the G-JF thermostat implementation in the ESPREesSo package, demonstrating improved configurational sampling accuracy in soft-matter simulations over existing methods.

Contribution

The paper presents a new G-JF thermostat implementation for Langevin Dynamics that enhances sampling accuracy without extensive code modifications.

Findings

G-JF thermostat reproduces near-correct statistics for all tested dt

It outperforms the existing integrator in the molecular package

Sampling deviations increase with larger dt in the previous method

Abstract

We implement the statistically sound G-JF thermostat for Langevin Dynamics simulations into the ESPREesSo molecular package for large-scale simulations of soft matter systems. The implemented integration method is tested against the integrator currently used by the molecular package in simulations of a fluid bilayer membrane. While the latter exhibits deviations in the sampling statistics that increase with the integration time step dt, the former reproduces near-correct configurational statistics for all dt within the stability range of the simulations. We conclude that, with very modest revisions to existing codes, one can significantly improve the performance of statistical sampling using Langevin thermostats.