Adaptive Equilibration of Molecular Dynamics Simulations

TL;DR

This paper introduces a systematic framework for automating and improving molecular dynamics equilibration by evaluating initialization methods, thermodynamic protocols, and uncertainty analysis, leading to more efficient and quantifiable equilibration procedures.

Contribution

It provides a comprehensive evaluation of initialization approaches and thermodynamic protocols, establishing temperature stability and uncertainty metrics as tools for automated equilibration.

Findings

Physics-informed initialization methods outperform others at high coupling strengths.

Weaker thermostat coupling reduces equilibration time.

OFF-ON thermostating sequences are generally more effective than ON-OFF.

Abstract

We present a systematic framework for shortening and automating molecular dynamics equilibration through improved position initialization methods and uncertainty quantification analysis, using the Yukawa one-component plasma as an exemplar system. Our comprehensive evaluation of seven initialization approaches (uniform random, uniform random with rejection, Halton and Sobol sequences, perfect and perturbed lattices, and a Monte Carlo pair distribution method) demonstrates that initialization significantly impacts equilibration efficiency, with microfield distribution analysis providing diagnostic insights into thermal behaviors. Our results establish that initialization method selection is relatively inconsequential at low coupling strengths, while physics-informed methods demonstrate superior performance at high coupling strengths, reducing equilibration time. We establish direct relationships between temperature stability and uncertainties in transport properties (diffusion coefficient and viscosity), comparing thermostating protocols including ON-OFF versus OFF-ON duty cycles, Berendsen versus Langevin thermostats, and thermostat coupling strengths. Our findings demonstrate that weaker thermostat coupling generally requires fewer equilibration cycles, and OFF-ON thermostating sequences outperform ON-OFF approaches for most initialization methods. The methodology implements temperature forecasting as a quantitative metric for system thermalization, enabling users to determine equilibration adequacy based on specified uncertainty tolerances in desired output properties, thus transforming equilibration from a heuristic process to a rigorously quantifiable procedure with clear termination criteria.