Hybrid deterministic and stochastic approach for efficient atomistic simulations at long time scales

TL;DR

This paper introduces a hybrid deterministic-stochastic method combining molecular dynamics and Monte Carlo simulations to extend atomistic simulation time scales efficiently, accurately capturing rare events and thermalization.

Contribution

A novel hybrid approach that enhances atomistic simulation efficiency by integrating MD and MC methods with control over accuracy and minimal computational overhead.

Findings

Accurate diffusivity predictions for vacancy-mediated diffusion in Fe.

Excellent agreement with experimental and theoretical data on Au nanopillars.

Significant orders-of-magnitude speedup over standard MD simulations.

Abstract

We propose a hybrid deterministic and stochastic approach to achieve extended time scales in atomistic simulations that combines the strengths of molecular dynamics (MD) and Monte Carlo (MC) simulations in an easy-to-implement way. The method exploits the rare event nature of the dynamics similar to most current accelerated MD approaches but goes beyond them by providing, without any further computational overhead, (a) rapid thermalization between infrequent events, thereby minimizing spurious correlations, and (b) control over accuracy of time-scale correction, while still providing similar or higher boosts in computational efficiency. We present two applications of the method: (a) Vacancy-mediated diffusion in Fe yields correct diffusivities over a wide range of temperatures and (b) source-controlled plasticity and deformation behavior in Au nanopillars at realistic strain rates (10^4/s and lower), with excellent agreement with previous theoretical predictions and in situ high-resolution transmission electron microscopy observations. The method gives several orders-of-magnitude improvements in computational efficiency relative to standard MD and good scalability with the size of the system.