An Event-Driven Hybrid Molecular Dynamics and Direct Simulation Monte Carlo Algorithm

TL;DR

This paper introduces a hybrid stochastic algorithm combining event-driven molecular dynamics and direct simulation Monte Carlo to efficiently simulate polymer-solvent systems, accurately capturing dynamics with significantly reduced computational cost.

Contribution

The novel SEDMD algorithm integrates EDMD and DSMC methods for polymer-solvent simulations, offering improved efficiency over traditional full MD approaches.

Findings

Algorithm closely reproduces full MD results

Achieves two orders of magnitude greater efficiency

Does not observe periodic polymer chain motion

Abstract

A novel Stochastic Event-Driven Molecular Dynamics (SEDMD) algorithm is developed for the simulation of polymer chains suspended in a solvent. The polymers are represented as chains of hard spheres tethered by square wells and interact with the solvent particles with hard core potentials. The algorithm uses Event-Driven Molecular Dynamics (EDMD) for the simulation of the polymer chain and the interactions between the chain beads and the surrounding solvent particles. The interactions between the solvent particles themselves are not treated deterministically as in event-driven algorithms, rather, the momentum and energy exchange in the solvent is determined stochastically using the Direct Simulation Monte Carlo (DSMC) method. The coupling between the solvent and the solute is consistently represented at the particle level, however, unlike full MD simulations of both the solvent and the solute, the spatial structure of the solvent is ignored. The algorithm is described in detail and applied to the study of the dynamics of a polymer chain tethered to a hard wall subjected to uniform shear. The algorithm closely reproduces full MD simulations with two orders of magnitude greater efficiency. Results do not confirm the existence of periodic (cycling) motion of the polymer chain.