Abstract
Version 13 of XtalOpt, an evolutionary algorithm for crystal structure prediction, is now available for download from the CPC program library or the XtalOpt website, https://xtalopt.github.io. In the new version of the XtalOpt code, a general platform for multi-objective global optimization is implemented. This functionality is designed to facilitate the search for (meta)stable phases of functional materials through minimization of the enthalpy of a crystalline system coupled with the simultaneous optimization of any desired properties that are specified by the user. The code is also able to perform a constrained search by filtering the parent pool of structures based on a user-specified feature, while optimizing multiple objectives. Here, we present the implementation and various technical details, and we provide a brief overview of additional improvements that have been introduced in the new version of XtalOpt. Program summaryProgram Title: XtalOptCPC Library link to program files:https://doi.org/10.17632/jt5pvnnm39.4Developer's repository link:https://github.com/xtalopt/XtalOptLicensing provisions: BSD 3-clauseProgramming language: C++.Journal reference of previous version: Comput. Phys. Commun. 237 (2019) 274–275.Does the new version supersede the previous version?: Yes.Reasons for the new version: Implementation of a multi-objective evolutionary search within the XtalOpt program package.Summary of revisions: Implemented a general user-friendly multi-objective search capability, made various improvements to user interface and functionalities, performed bug fixes.Nature of problem: The XtalOpt algorithm is designed to search for (meta)stable crystal structures, optionally with specific functionalities – a grand challenge in computational materials science, chemistry and physics.Solution method: A generalized scalar fitness function, where a set of user-specified objectives contribute to the fitness value for candidate structures, is implemented within XtalOpt. This generalized fitness biases the search towards the discovery of (meta)stable phases with structural motifs that are key for the desired characteristics. As a result, the evolutionary search explores regions of the energy landscape of higher relevance in terms of target properties.
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