Random-batch list algorithm for short-range molecular dynamics simulations

TL;DR

This paper introduces a stochastic random-batch list method that accelerates short-range molecular dynamics simulations by reducing interaction calculations, maintaining accuracy, and being easy to implement and extend.

Contribution

The paper presents a novel stochastic neighbor-list algorithm that significantly speeds up molecular dynamics simulations without sacrificing accuracy.

Findings

Achieves several-fold speedup in Lennard-Jones fluid simulations

Maintains accuracy comparable to classical methods

Easy to integrate with existing simulation frameworks

Abstract

We propose a fast method for the calculation of short-range interactions in molecular dynamics simulations. The so-called random-batch list method is a stochastic version of the classical neighbor-list method to avoid the construction of a full Verlet list, which introduces two-level neighbor lists for each particle such that the neighboring particles are located in core and shell regions, respectively. Direct interactions are performed in the core region. For the shell zone, we employ a random batch of interacting particles to reduce the number of interaction pairs. The error estimate of the algorithm is provided. We investigate the Lennard-Jones fluid by molecular dynamics simulations, and show that this novel method can significantly accelerate the simulations with a factor of several fold without loss of the accuracy. This method is simple to implement, can be well combined with any linked cell methods to further speed up and scale up the simulation systems, and can be straightforwardly extended to other interactions such as Ewald short-range part, and thus it is promising for large-scale molecular dynamics simulations.