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

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.