XtalOpt Version 14: Variable-Composition Crystal Structure Search for Functional Materials Through Pareto Optimization

TL;DR

XtalOpt Version 14 introduces a multi-objective evolutionary algorithm with Pareto optimization for crystal structure prediction, enabling efficient discovery of functional materials with variable compositions using ab initio, classical, and machine-learning methods.

Contribution

The paper presents the new Version 14 of XtalOpt, which incorporates Pareto optimization and supports variable compositions for enhanced crystal structure prediction.

Findings

Enhanced multi-objective search with Pareto optimization

Supports variable composition crystal structure prediction

Integrates ab initio, classical, and machine-learning potentials

Abstract

Version 14 of XtalOpt, an evolutionary multi-objective global optimization algorithm for crystal structure prediction, is now available for download from its official website https://xtalopt.github.io, and the Computer Physics Communications Library. The new version of the code is designed to perform a ground state search for crystal structures with variable compositions by integrating a suite of ab initio methods alongside classical and machine-learning potentials for structural relaxation. The multi-objective search framework has been enhanced through the introduction of Pareto optimization, enabling efficient discovery of functional materials. Herein, we describe the newly implemented methodologies, provide detailed instructions for their use, and present an overview of additional improvements included in the latest version of the code.