PyXtal: A Python library for crystal structure generation and symmetry analysis

Fredericks, Scott; Parrish, Kevin; Sayre, Dean; Zhu, Qiang

doi:10.1016/j.cpc.2020.107810

Cited by 64 publications

(50 citation statements)

References 38 publications

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“…• COMBO • pymatgen • PyXtal COMBO [26] is an efficient Bayesian optimization library, pymatgen [27] has many features in materials analysis, and PyXtal [28] is useful for crystal structure generation and symmetry analysis. First-principles or classical interatomic potential codes are also needed for energy evaluation and local structure optimization.…”

Section: System Requirementmentioning

confidence: 99%

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CrySPY: a crystal structure prediction tool accelerated by machine learning

Yamashita

Kanehira

Sato

et al. 2021

Science and Technology of Advanced Materials: Methods

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We have developed an open-source software called CrySPY, which is a crystal structure prediction tool written in Python 3, and runs on Unix/Linux platforms. CrySPY enables anyone to easily perform crystal structure prediction simulations for materials discovery and design, and automates structure generation, structure optimization, energy evaluation, and efficiently selecting candidates using machine learning. Several searching algorithms are available such as random search, evolutionary algorithm, Bayesian optimization, and Look Ahead based on Quadratic Approximation. Machine learning is employed to efficiently select candidates for priority optimization. CrySPY does not require complex machine learning techniques for users. In the latest version of CrySPY, both atomic and molecular random structures can be generated. CrySPY supports VASP, QUANTUM ESPRESSO, OpenMX, soiap, and LAMMPS for local structure optimization and energy evaluation.

show abstract

Section: System Requirementmentioning

confidence: 99%

“…CrySPY has two modes of random structure generation: atomic crystal structure generation and molecular crystal structure generation. The latest version of CrySPY employs PyXtal library [28] as default to generate such random structures.…”

Section: Random Searchmentioning

confidence: 99%

CrySPY: a crystal structure prediction tool accelerated by machine learning

Yamashita

Kanehira

Sato

et al. 2021

Science and Technology of Advanced Materials: Methods

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“…Here we propose a multi-objective crystal structure prediction algorithm based on contact map, coordination number of the cation and ages of individuals as shown in Figure 2. First, we use the PyXtal library [39] to generate 50 random crystal structures based on the given formula and the space group. We then select five suitable random crystal structures, and use the multi-objective crystal structure prediction algorithm to search the Wyckoff positions coordinates by approximating the target contact map, the coordination number of the cation and minimizing the ages of individuals.…”

Section: B Age-fitness Based Multi-objective Genetic Algorithm For Cr...mentioning

confidence: 99%

“…To predict the crystal structure from multi-objective genetic algorithm, we need a seed structure as starting point to optimize. Here we use PyXtal [39] to generate candidate template structures.…”

Section: A Generating and Screening Random Crystal Structures With A ...mentioning

confidence: 99%

Crystal structure prediction using age-fitness multi-objective genetic algorithm and coordination number constraints

Yang,

Siriwardane,

2021

Preprint

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Crystal structure prediction (CSP) has emerged as one of the most important approaches for discovering new materials. CSP algorithms based on evolutionary algorithms and particle swarm optimization have discovered a great number of new materials. However, these algorithms based on ab initio calculation of free energy are inefficient. Moreover, they have severe limitations in terms of scalability. We recently proposed a promising crystal structure prediction method based on atomic contact maps, using global optimization algorithms to search for the Wyckoff positions by maximizing the match between the contact map of the predicted structure and the contact map of the true crystal structure. However, our previous contact map based CSP algorithms have two major limitations: (1) the loss of search capability due to getting trapped in local optima; (2) it only uses the connection of atoms in the unit cell to predict the crystal structure, ignoring the chemical environment outside the unit cell, which may lead to unreasonable coordination environments. Herein we propose a novel multi-objective genetic algorithms for contact map-based crystal structure prediction by optimizing three objectives, including contact map match accuracy, the individual age, and the coordination number match. Furthermore, we assign the age values to all the individuals of the GA and try to minimize the age aiming to avoid the premature convergence problem. Our experimental results show that compared to our previous CMCrystal algorithm, our multi-objective crystal structure prediction algorithm (CMCrystalMOO) can reconstruct the crystal structure with higher quality and alleviate the problem of premature convergence.

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“…The provided Python (Van Rossum & Drake, 2009) program requires the Atomic Simulation Environment (ASE) library (Larsen et al, 2017) for CIF-parsing as well as Spglib (Togo & Tanaka, 2018), PyXtal (Fredericks et al, 2019) and numpy (van der Walt et al, 2011) for symmetry calculations.…”

Section: Utilized Software and Databasesmentioning

confidence: 99%

crystIT: Complexity and Configurational Entropy of Crystal Structures via Information Theory

Kaußler¹,

Kieslich

2020

Preprint

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<div>The information content of a crystal structure as conceived by information theory has recently proved as an intriguing approach to calculate the complexity of a crystal structure within a consistent concept. Given the relatively young nature of the field, theory development is still at the core of on-going research efforts. In this work we provide an update to the current theory, improving the formulas that evaluation of crystal structures with partial occupancies as frequently found in disordered system is feasible. To encourage wider application and further theory development we incorporate the updated formulas in crystIT (crystal structure & Information Theory), an open-source python-based program that allows for calculating various complexity measures of crystal structures based on a standardized *.cif file.</div>

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PyXtal: A Python library for crystal structure generation and symmetry analysis

Cited by 64 publications

References 38 publications

CrySPY: a crystal structure prediction tool accelerated by machine learning

CrySPY: a crystal structure prediction tool accelerated by machine learning

Crystal structure prediction using age-fitness multi-objective genetic algorithm and coordination number constraints

crystIT: Complexity and Configurational Entropy of Crystal Structures via Information Theory

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