Relative Resolution: A Computationally Efficient Implementation in LAMMPS

TL;DR

This paper presents an efficient implementation of the Relative Resolution multiscale simulation method in LAMMPS, enabling accurate and faster modeling of Lennard-Jones systems like alkane liquids and copolymers.

Contribution

The work introduces a practical, automated RelRes algorithm in LAMMPS that significantly improves computational efficiency for Lennard-Jones based simulations.

Findings

Nearly tenfold increase in simulation speed.

Validation across various alkane liquids and copolymer solutions.

Accurate reproduction of structural and dynamic properties.

Abstract

Recently, a novel type of a multiscale simulation, called Relative Resolution (RelRes), was introduced. In a single system, molecules switch their resolution in terms of their relative separation, with near neighbors interacting via fine-grained potentials yet far neighbors interacting via coarse-grained potentials; notably, these two potentials are analytically parameterized by a multipole approximation. This multiscale approach is consequently able to correctly retrieve across state space, the structural and thermal, as well as static and dynamic, behavior of various nonpolar mixtures. Our current work focuses on the practical implementation of RelRes in LAMMPS, specifically for the commonly used Lennard-Jones potential. By examining various correlations and properties of several alkane liquids, including complex solutions of alternate cooligomers and block copolymers, we confirm the validity of this automated LAMMPS algorithm. Most importantly, we demonstrate that this RelRes implementation gains almost an order of magnitude in computational efficiency, as compared with conventional simulations. We thus recommend this novel LAMMPS algorithm for anyone studying systems governed by Lennard-Jones interactions.