Document

CrySPY (pronounced as crispy) is a crystal structure prediction tool written in Python.
CrySPY automates the following:

• Structure generation
• Submitting jobs for structure optimization
• Collecting data for structure optimization
• Selecting candidates using machine learning

CrySPY can be install by pip install csp-cryspy.

Latest version

CrySPY 1.2.3 (2023 October 21)

News

• [2023 October 21] CrySPY 1.2.3 released. Version information/version 1.2.3
  • Bug fix for MPI
• [2023 October 18] CrySPY 1.2.2 released. Version information/version 1.2.2
  • Enthalpy
• [2023 September 27] CrySPY 1.2.1 released. Version information/version 1.2.1
  • Bug fix for ASE interface
• [2023 August 8] (Document, Utility) Upload the example of CHGNET
• [2023 July 10] CrySPY 1.2.0 released. Version information/version 1.2.0
  • Interface for ASE
  • Adoption of logging
• [2023 June 14] CrySPY 1.1.1 released. Version information/version 1.1.1
  • bug fix
• [2023 May 16] CrySPY 1.1.0 released. Version information/version 1.1.0
  • MPI parallelization (optional)
  • New score of LAQA
• [2023 March 16] CrySPY 1.0.0 released. Version information/version 1.0.0
  • CrySPY is available in PyPI, so you can install by pip.

Discussions

Discussions in GitHub (questions and comments)

License

CrySPY is distributed under the MIT License
Copyright (c) 2018 CrySPY Development Team

Code contributors

• Tomoki Yamashita and Lab members (Nagaoka University of Technology)
• Nobuya Sato (Tokyo Institute of Technology)
• Hiori Kino (National Institute for Materials Science)
• Kei Terayama (Yokohama City University)
• Hikaru Sawahata (Kanazawa University)
• Shinichi Kanehira (Osaka University)

Reference

• CrySPY（software）
  • T. Yamashita, S. Kanehira, N. Sato, H. Kino, H. Sawahata, T. Sato, F. Utsuno, K. Tsuda, T. Miyake, and T. Oguchi,
    “CrySPY: a crystal structure prediction tool accelerated by machine learning”,
    Sci. Technol. Adv. Mater. Meth. 1, 87 (2021). Link
• Bayesian optimization
  • T. Yamashita, N. Sato, H. Kino, T. Miyake, K. Tsuda, and T. Oguchi,
    “Crystal structure prediction accelerated by Bayesian optimization”,
    Phys. Rev. Mater. 2, 013803 (2018). Link
  • N. Sato, T. Yamashita, T. Oguchi, K. Hukushima, and T. Miyake,
    “Adjusting the descriptor for a crystal structure search using Bayesian optimization”,
    Phys. Rev. Mater. 4, 033801 (2020). Link
• Bayesian optimization and evolutionary algorithm
  • T. Yamashita, H. Kino, K. Tsuda, T. Miyake, and T. Oguchi,
    “Hybrid algorithm of Bayesian optimization and evolutionary algorithm in crystal structure prediction”,
    Sci. Technol. Adv. Mater. Meth. 2, 67 (2022). Link
• LAQA
  • K.Terayama, T. Yamashita, T. Oguchi, and K. Tsuda,
    “Fine-grained optimization method for crystal structure prediction”,
    npj Comput. Mater. 4, 32 (2018). Link
  • T. Yamashita and H. Sekine,
    “Improvement of look ahead based on quadratic approximation for crystal structure prediction”,
    Sci. Technol. Adv. Mater. Meth. 2, 84 (2022). Link

Link

GitHub repo GitHub discussions CrySPY utility