Optimization of Coulomb Energies in Gigantic Configurational Spaces of Multi-Element Ionic Crystals

TL;DR

This paper introduces GOAC, an efficient optimization tool that rapidly identifies low-energy configurations in complex multi-element ionic crystals with enormous configurational spaces, aiding materials design.

Contribution

The paper presents a novel Coulomb energy optimizer using a second-order cluster expansion combined with Monte Carlo and Genetic Algorithms, significantly improving speed and scalability.

Findings

Achieves several orders of magnitude speed-up over existing methods.

Capable of handling up to 10^920 configurations.

Effectively finds ground-state structures in complex multi-element systems.

Abstract

Most of the novel energy materials contain multiple elements occupying a single site in their lattice. The exceedingly large configurational space of these materials imposes challenges in determining their ground-state structures. Coulomb energies of possible configurations generally show a satisfactory correlation to computed energies at higher levels of theory and thus allow to screen for minimum-energy structures. Employing a second-order cluster expansion, we obtain an efficient Coulomb energy optimizer using Monte Carlo and Genetic Algorithms. The presented optimization package, GOAC (Global Optimization of Atomistic Configurations by Coulomb), can achieve a speed up of several orders of magnitude compared to existing software. Our code is able to find low-energy configurations of complex systems involving up to $10^{920}$ structural configurations. The GOAC package thus provides an efficient method for constructing ground-state atomistic models for multi-element materials with gigantic configurational spaces.