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.

Structural properties of transition-metal clusters via force-biased Monte Carlo and ab initio calculations: A comparative study

“…In recent years, there has been rapid progress in the development of global optimization techniques, which encompass state-of-the-art evolutionary computing 1 to the populationbased swarm intelligence and differential-evolution approaches. 2,3 Despite this development, Monte Carlo (MC) methods, based on simple Metropolis and related algorithms, continue to play a major role in addressing optimization problems in science and technology. In the context of structural modeling of amorphous solids [4][5][6] on the atomistic length scale, the Monte Carlo procedure is particularly useful for optimization of a total-energy functional without any knowledge of atomic forces or local gradients of the energy functional.…”