PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis

Abstract

We present PyXtal, a new package based on the Python programming language, used to generate structures with specific symmetry and chemical compositions for both atomic and molecular systems. This soft ware provides support for various systems described by point, rod, layer, and space group symmetries. With only the inputs of chemical composition and symmetry group information, PyXtal can automatically find a suitable combination of Wyckoff positions with a step-wise merging scheme. Further, when the molecular geometry is given, PyXtal can generate different dimensional organic crystals with molecules occupying both general and special Wyckoff positions. Optionally, PyXtal also accepts user-defined parameters (e.g., cell parameters, minimum distances and Wyckoff positions). In general, PyXtal serves three purposes: (1) to generate custom structures, (2) to modulate the structure by symmetry relations, (3) to interface the existing structure prediction codes that require the generation of random symmetric structures. In addition, we provide several utilities that facilitate the analysis of structures, including symmetry analysis, geometry optimization, and simulations of powder X-ray diffraction (XRD). Full documentation of PyXtal is available at \url{https://pyxtal.readthedocs.io}.

arXiv comment10 pages, 8 figures

References in corpus (11)

• Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms
• CALYPSO: a method for crystal structure prediction
• Crystal Structure Prediction via Particle Swarm Optimization
• Ab initio Random Structure Searching
• Predicting crystal structures: the Parrinello-Rahman method revisited
• Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
• Data-driven learning of total and local energies in elemental boron
• Constrained evolutionary algorithm for structure prediction of molecular crystals: methodology and applications
• $\text{Spglib}$: a software library for crystal symmetry search
• Genarris 2.0: A Random Structure Generator for Molecular Crystals
• Prediction of Novel High Pressure H2O-NaCl and Carbon Oxide Compounds with Symmetry-Driven Structure Search Algorithm

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