A genetic algorithm for the atomistic design and global optimisation of substitutionally disordered materials

TL;DR

This paper introduces a symmetry-adapted genetic algorithm that effectively designs and optimizes substitutionally disordered materials, outperforming previous methods without requiring redesign.

Contribution

It presents a novel symmetry-adapted crossover technique that prevents premature convergence in genetic algorithms for disordered materials.

Findings

Outperforms Monte Carlo and traditional genetic algorithms in finding low energy minima

Effectively handles substitutionally disordered bulk materials and surfaces

No need for redesign when applied to simple alloy models

Abstract

We present a genetic algorithm for the atomistic design and global optimisation of substitutionally disordered bulk materials and surfaces. Premature convergence which hamper conventional genetic algorithms due to problems with synchronisation is avoided using a symmetry adapted crossover. The algorithm outperforms previously reported Monte Carlo and genetic algorithm simulations for finding low energy minima of two simple alloy models without the need for any redesign.