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CrySPY 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

License

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

Latest version

CrySPY 0.10.2 (2022 January 24)

News

Click to expand old news.

  • [2022 January 24] version 0.10.2 released
    • Added nrot: maximum number of times to rotate molecules in mol_bs
  • [2021 September 30] version 0.10.1 released
    • Fixed the problem of numpy.random.seed in multiprocessing
  • [2021 July 25] version 0.10.0 released
    • Support PyXtal 0.2.9 or later
    • LAQA can be used with QE
    • Upper and lower limits of energy for EA and BO
  • [2021 July 13] paper published
    • Our paper on CrySPY software has been published in STAM:Methods
  • [2021 March 18] version 0.9.2 released
    • Support pymatgen v2022.
  • [2021 February 7] version 0.9.0 released
  • [2020 March 19] paper published
  • [2020 February 16] version 0.8.0 released
  • [2018 December 5] version 0.7.0 released
  • [2018 August 20] version 0.6.4 released
  • [2018 July 2] version 0.6.3 released
  • [2018 June 26] Version 0.6.2 released
  • [2018 March 1] Version 0.6.1 released
  • [2018 January 9] paper published

Code contributors

  • Tomoki Yamashita (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)
  • Takumi Sato (Nagaoka University of Technology)
  • Hirotaka Sekine (Nagaoka University of Technology)

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

GitHub repo GitHub release CrySPY utility Google group