Article CrySPY: a crystal structure prediction tool accelerated by machine learning

Tomoki Yamashita ; Shinichi Kanehira ; Nobuya Sato ; Hiori Kino ORCID (National Institute for Materials ScienceROR) ; Kei Terayama ; Hikaru Sawahata ; Takumi Sato ; Futoshi Utsuno ; Koji Tsuda SAMURAI ORCID (National Institute for Materials ScienceROR) ; Takashi Miyake SAMURAI ORCID (National Institute for Materials ScienceROR) ; Tamio Oguchi

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Tomoki Yamashita, Shinichi Kanehira, Nobuya Sato, Hiori Kino, Kei Terayama, Hikaru Sawahata, Takumi Sato, Futoshi Utsuno, Koji Tsuda, Takashi Miyake, Tamio Oguchi. CrySPY: a crystal structure prediction tool accelerated by machine learning. Science and Technology of Advanced Materials: Methods. 2021, 1 (1), 87-97.
SAMURAI

Description:

(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 gener-
ated. 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.

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Keyword: crystal structure prediction, Bayesian optimization, LAQA, first-principles calculations, evolutionary algorithm

Date published: 2021-01-01

Publisher: Informa UK Limited

Journal:

  • Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 1 issue. 1 p. 87-97

Funding:

  • Support Program for Starting Up Innovation Hub Materials research by Information Integration Initiative (MI 2 I) project
  • JST JPMJCR1502
  • MEXT CDMSI
  • JSPS KAKENHI JP18K13474
  • JSPS KAKENHI JP20J13011

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1080/27660400.2021.1943171

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Updated at: 2024-04-02 23:51:18 +0900

Published on MDR: 2023-02-10 10:31:36 +0900

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