CrySPY: a crystal structure prediction tool accelerated by machine learning
CrySPY: a crystal structure prediction tool accelerated by machine learning
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DOI：
10.1080/27660400.2021.1943171
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发表时间：
2021-01
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影响因子：
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通讯作者：
T. Yamashita;S. Kanehira;N. Sato;H. Kino;Kei Terayama;Hikaru Sawahata;Takumi Sato;F. Utsuno;Koji Tsuda;Takashi Miyake;T. Oguchi
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作者：
T. Yamashita;S. Kanehira;N. Sato;H. Kino;Kei Terayama;Hikaru Sawahata;Takumi Sato;F. Utsuno;Koji Tsuda;Takashi Miyake;T. Oguchi
摘要我们开发了一个开源的晶体结构预测软件CrySPY，它是用Python 3编写的，运行在Unix/Linux平台上。CrySPY使任何人都可以轻松地进行晶体结构预测模拟，用于材料发现和设计，并自动化结构生成，结构优化，能量评估，并使用机器学习有效地选择候选人。几种搜索算法是可用的，如随机搜索，进化算法，贝叶斯优化，和基于二次近似的向前看。采用机器学习来有效地选择用于优先级优化的候选者。CrySPY不需要复杂的机器学习技术。在最新版本的CrySPY中，可以生成原子和分子随机结构。CrySPY支持VASP、QUANTUM ESTERO、OpenMX、soiap和LAMMPS进行局部结构优化和能量评估。CrySPY在https://github.com/Tomoki-YAMASHITA/CrySPY上根据MIT许可证发布。CrySPY的文档也可以在https://Tomoki-YAMASHITA.github.io/CrySPY_doc上找到。图形摘要
ABSTRACT 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. CrySPY is distributed under the MIT license at https://github.com/Tomoki-YAMASHITA/CrySPY. Documentation of CrySPY is also available at https://Tomoki-YAMASHITA.github.io/CrySPY_doc. Graphical Abstract