Efficient structural relaxation of polycrystalline graphene models

TL;DR

This paper presents a fast, efficient method for relaxing large polycrystalline graphene models by modifying existing algorithms with early rejection and decision techniques, significantly reducing computational time.

Contribution

It introduces an early rejection variation and an early decision variation of the Wooten-Winer-Weaire method tailored for graphene, enabling faster relaxation of large samples without losing accuracy.

Findings

Achieved 10- to 100-fold speedup in sample relaxation

Successfully relaxed samples with up to 20,000 atoms

Provided a graphical tool for artifact removal in samples

Abstract

Large samples of experimentally produced graphene are polycrystalline. For the study of this material, it helps to have realistic computer samples that are also polycrystalline. A common approach to produce such samples in computer simulations is based on the method of Wooten, Winer, and Weaire, originally introduced for the simulation of amorphous silicon. We introduce an early rejection variation of their method, applied to graphene, which exploits the local nature of the structural changes to achieve a significant speed-up in the relaxation of the material, without compromising the dynamics. We test it on a 3,200 atoms sample, obtaining a speedup between one and two orders of magnitude. We also introduce a further variation called early decision specifically for relaxing large samples even faster and we test it on two samples of 10,024 and 20,000 atoms, obtaining a further speed-up of an order of magnitude. Furthermore, we provide a graphical manipulation tool to remove unwanted artifacts in a sample, such as bond crossings.