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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 0.9.2 (2021 March 18)

News

Click to expand old news.

  • [2021 March 18] version 0.9.2 released
    • Support pymatgen v2022.
  • [2021 February 7] version 0.9.0 released
    • Interfaced with OpenMX
    • Employ PyXtal library to generate initial structures
    • If you use PyXtal (default), find_wy program is not required
    • LAQA can be used with soiap
    • Change the name: [lattice] section –> [structure] section
    • Several input variables move to [structure] section
      • natot: [basic] –> [structure]
      • atype: [basic] –> [structure]
      • nat: [basic] –> [structure]
      • maxcnt: [option] –> [structure]
      • symprec: [option] –> [structure]
      • spgnum: [option] –> [structure]
    • New features
      • Molecular crystal structure generation
      • Scale volume
  • [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)

Reference

  • 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
  • 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

GitHub repo GitHub release CrySPY utility Google group