GenSym: A tool for random symmetric structure generation and its application in crystal structure prediction software

Crystal structure prediction (CSP) methods have become essential tools in materials discovery. As the initial step in CSP, the quality of the generated structures critically determines efficiency. It has been increasingly recognized that incorporating symmetry during structure generation can significantly enhance efficiency, as most real materials crystallize in non-P1 symmetries. In this work, we present an open-source package for random symmetric structure generation under user-defined constraints. The program supports bulk, low-dimensional, and molecular crystal generation, with flexible control over parameters such as cell shape, bond length, conventional or primitive symmetry settings, Wyckoff position weighting, and density uniformity adjustment. Furthermore, it interfaces with prototype databases, enabling structure generation based on known prototypes. We demonstrate that its integration with CSP frameworks substantially improves search efficiency, offering strong potential for accelerating novel materials design and discovery.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2026-03-24
DOI
https://doi.org/10.1063/5.0322405
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

GenSym: A tool for random symmetric structure generation and its application in crystal structure prediction software

The Journal of Chemical Physics
Machine Learning in Materials Science
article

GenSym: A tool for random symmetric structure generation and its application in crystal structure prediction software

article en

Abstract

Crystal structure prediction (CSP) methods have become essential tools in materials discovery. As the initial step in CSP, the quality of the generated structures critically determines efficiency. It has been increasingly recognized that incorporating symmetry during structure generation can significantly enhance efficiency, as most real materials crystallize in non-P1 symmetries. In this work, we present an open-source package for random symmetric structure generation under user-defined constraints. The program supports bulk, low-dimensional, and molecular crystal generation, with flexible control over parameters such as cell shape, bond length, conventional or primitive symmetry settings, Wyckoff position weighting, and density uniformity adjustment. Furthermore, it interfaces with prototype databases, enabling structure generation based on known prototypes. We demonstrate that its integration with CSP frameworks substantially improves search efficiency, offering strong potential for accelerating novel materials design and discovery.

The Journal of Chemical PhysicsVol. 164(12)
Collaborative Innovation Center of Advanced Microstructures (CN), Donostia International Physics Center (ES)
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Machine Learning in Materials Science
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