csp-cryspy 1.4.0

CrySPY is a crystal structure prediction tool written in Python.

CrySPY

CrySPY (pronounced as crispy) is a crystal structure prediction tool written in Python.
Document: https://tomoki-yamashita.github.io/CrySPY_doc
Questions and comments: https://github.com/Tomoki-YAMASHITA/CrySPY/discussions

Latest version

version 1.4.0 (2025 June 17)

News

• [2025 June 17] CrySPY 1.4.0 released.
  • Support variable-composition evolutionary algorithm
  • Support interactive mode with Jupyter
  • There are important changes. See Version information
• [2024 May 31] CrySPY 1.3.0 released.
  • There are important changes. See Version information
• [2024 May 10] CrySPY 1.2.5 released.
  • bug fix for order_ef in out_results.py
• [2024 May 7] CrySPY 1.2.4 released.
  • bug fix
• [2023 October 21] CrySPY 1.2.3 released.
  • bug fix for MPI
• [2023 October 18] CrySPY 1.2.2 released.
  • Enthalpy
• [2023 September 27] CrySPY 1.2.1 released.
  • bug fix for ASE interface
• [2023 July 10] CrySPY 1.2.0 released. Version information/version 1.2.0
  • Interface for ASE
  • Adoption of logging
  • See also Version information and CHANGELOG
• [2023 June 14] CrySPY 1.1.1 released
  • bug fix
• [2023 May 16] CrySPY 1.1.0 released
  • MPI parallelization (optional)
  • New score of LAQA
  • See also Version information and CHANGELOG
• [2023 March 16] CrySPY 1.0.0 released
  • CrySPY is available in PyPI, so you can install by pip (project name is csp-cryspy).
  • See also Version information and CHANGELOG

System requirements

Python

• Python >= 3.8
• PyXtal >= 0.5.3

(optional)

• PHYSBO (required if algo is BO)
• DScribe (required if algo is BO)
• mpi4py
• nglview

See CrySPY document in detail.

Structure optimizer

At least one optimizer is required.

• VASP (tested with version 5.4.4)
• QUANTUM ESPRESSO (tested with version 6.x, version 5.x does not work)
• OpenMX
• soiap (tested with version 0.2.2)
• LAMMPS
• ASE

Document (English/Japanese)

CrySPY document

CrySPY Utility

CrySPY Utility

Reference

CrySPY (software)

• T. Yamashita, S. Kanehira, N. Sato, H. Kino, H. Sawahata, T. Sato, F. Utsuno, K. Tsuda, T. Miyake, and T. Oguchi, Sci. Technol. Adv. Mater. Meth. 1, 87 (2021).
  • https://www.tandfonline.com/doi/full/10.1080/27660400.2021.1943171

Bayesian optimization

• T. Yamashita, N. Sato, H. Kino, T. Miyake, K. Tsuda, and T. Oguchi, Phys. Rev. Mater. 2, 013803 (2018).

  • https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.2.013803

• N. Sato, T. Yamashita, T. Oguchi, K. Hukushima, and T. Miyake, Phys. Rev. Mater. 4, 033801 (2020).

  • https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.4.033801

Baysian optimization and evolutionary algorithm

• T. Yamashita, H. Kino, K. Tsuda, T. Miyake, and T. Oguchi, Sci. Technol. Adv. Mater. Meth. 2, 67 (2022).
  • https://www.tandfonline.com/doi/full/10.1080/27660400.2022.2055987

LAQA

• K.Terayama, T. Yamashita, T. Oguchi, and K. Tsuda, npj Comput. Mater. 4, 32 (2018).

  • https://www.nature.com/articles/s41524-018-0090-y

• T. Yamashita and H. Sekine, Sci. Technol. Adv. Mater. Meth. 2, 84 (2022).

  • https://www.tandfonline.com/doi/full/10.1080/27660400.2022.2059335

License

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