Optimization of Coulomb energies in gigantic configurational spaces of multi-element ionic crystals

• Published in: npj computational materials Vol. 11; no. 1; pp. 202 - 13
• Main Authors: Köster, Konstantin, Binninger, Tobias, Kaghazchi, Payam
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1034/1035; 639/301/1034/1037; Approximation; Characterization and Evaluation of Materials; Chemistry and Materials Science; Computational Intelligence; Configurations; Crystals; Energy; Genetic algorithms; Global optimization; Heuristic; Ionic crystals; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Optimization techniques; Python; Symmetry; Theoretical
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-025-01690-7

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 low(est) energy 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 an expansion into a binary optimization problem, 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. In this work, heuristic optimization on various material classes is performed. Thus, GOAC provides an efficient method for constructing low-energy atomistic models for ionic multi-element materials with gigantic configurational spaces.