A thermally-driven differential mutation approach for the structural optimization of large atomic systems

TL;DR

This paper introduces a novel thermally-driven differential mutation genetic algorithm combined with simulated annealing for efficiently finding low-energy structures in large amorphous atomic systems, demonstrated on amorphous graphene.

Contribution

It presents a new optimization method merging differential mutation with thermal selection, effective for multimodal structural optimization with small populations.

Findings

Successfully optimized amorphous graphene structures with small populations.

Obtained low-energy, diverse atomic arrangements with similar physical properties.

Method is reliable for complex, multimodal energy landscapes.

Abstract

A computational method is presented which is capable to obtain low lying energy structures of topological amorphous systems. The method merges a differential mutation genetic algorithm with simulated annealing. This is done by incorporating a thermal selection criterion, which makes it possible to reliably obtain low lying minima with just a small population size and is suitable for multimodal structural optimization. The method is tested on the structural optimization of amorphous graphene from unbiased atomic starting configurations. With just a population size of six systems, energetically very low structures are obtained. While each of the structures represents a distinctly different arrangement of the atoms, their properties, such as energy, distribution of rings, radial distribution function, coordination number and distribution of bond angles, are very similar.