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

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.

References in corpus (18)

• Crystal Structure Prediction via Particle Swarm Optimization
• AFLOW: An automatic framework for high-throughput materials discovery
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• Machine learning modeling of superconducting critical temperature
• Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
• Stability of xenon oxides at high pressures
• Unified Graph Neural Network Force-field for the Periodic Table
• Deep learning generative model for crystal structure prediction
• Data-Driven Prediction of Complex Crystal Structures of Dense Lithium
• MAISE: Construction of neural network interatomic models and evolutionary structure optimization
• An Improved Real--Space Genetic Algorithm for Crystal Structure and Polymorph Prediction
• Developments and Further Applications of Ephemeral Data Derived Potentials
• Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database
• XtalOpt Version 13: Multi-Objective Evolutionary Search for Novel Functional Materials
• The Microscopic Diamond Anvil Cell: Stabilization of Superhard, Superconducting Carbon Allotropes at Ambient Pressure
• StructOpt: A modular materials structure optimization suite incorporating experimental data and simulated energies
• Experimentally informed structure optimization of amorphous TiO2 films grown by atomic layer deposition
• Structurally Constrained Evolutionary Algorithm for the Discovery and Design of Metastable Phases