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

https://mdr.nims.go.jp/datasets/f6ae9615-b564-4c7f-95aa-367a9e442507

## File

- [CrySPY a crystal structure prediction tool accelerated by machine learning.pdf](https://mdr.nims.go.jp/filesets/84887ca7-1964-43cb-b49c-730c5b347e87/download) ([Detail](https://mdr.nims.go.jp/filesets/84887ca7-1964-43cb-b49c-730c5b347e87.md))

## Id

f6ae9615-b564-4c7f-95aa-367a9e442507

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-02-09T01:19:10.336692Z

## Updated at

2024-04-02T14:51:18.926821Z

## Published at

2023-02-10T01:31:36.223462Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2021.1943171

## Date published

2021-01-01

## Recorded date published

2021-1-1

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: 'CrySPY: a crystal structure prediction tool accelerated by machine learning'
  title_type: original
  lang: en

## Description

- description: "We  have  developed  an  open-source  software  called  CrySPY,  which
    \ is  a  crystal  structure \r\nprediction tool written in Python 3, and runs
    on Unix/Linux platforms. CrySPY enables anyone \r\nto easily perform crystal structure
    prediction simulations for materials discovery and design, \r\nand automates structure
    generation, structure optimization, energy evaluation, and efficiently \r\nselecting
    candidates using machine learning. Several searching algorithms are available
    such \r\nas random search, evolutionary algorithm, Bayesian optimization, and
    Look Ahead based on \r\nQuadratic  Approximation.  Machine  learning  is  employed
    \ to  efficiently  select  candidates  for \r\npriority optimization. CrySPY does
    not require complex machine learning techniques for users. \r\nIn the latest version
    of CrySPY, both atomic and molecular random structures can be gener-\r\nated.
    \ CrySPY  supports  VASP,  QUANTUM  ESPRESSO,  OpenMX,  soiap,  and  LAMMPS  for
    \ local \r\nstructure optimization and energy evaluation. CrySPY is distributed
    under the MIT license at \r\nhttps://github.com/Tomoki-YAMASHITA/CrySPY. Documentation
    of CrySPY is also available at \r\nhttps://Tomoki-YAMASHITA.github.io/CrySPY_doc."
  description_type: abstract
  lang: eng

## Creator

- name: Tomoki Yamashita
  role: author
- name: Shinichi Kanehira
  role: author
- name: Nobuya Sato
  role: author
- name: Hiori Kino
  role: author
  orcid: https://orcid.org/0000-0002-8912-686X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Kei Terayama
  role: author
- name: Hikaru Sawahata
  role: author
- name: Takumi Sato
  role: author
- name: Futoshi Utsuno
  role: author
- name: Koji Tsuda
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Takashi Miyake
  role: author
  orcid: https://orcid.org/0000-0003-2658-3470
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Tamio Oguchi
  role: author

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: crystal structure prediction
  schema: not_defined
- subject: Bayesian optimization
  schema: not_defined
- subject: LAQA
  schema: not_defined
- subject: first-principles  calculations
  schema: not_defined
- subject: evolutionary  algorithm
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '1'
  issue: '1'
  start_page: 87
  end_page: 97

## Conference



## Related item



## Funding

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

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 84887ca7-1964-43cb-b49c-730c5b347e87
  filename: CrySPY a crystal structure prediction tool accelerated by machine learning.pdf
  content_type: application/pdf
  size: 5328437
  md5: 3428d8a98d68da83032b8048cdc6a98b

## Thumbnail

fileset_id: 84887ca7-1964-43cb-b49c-730c5b347e87
filename: CrySPY a crystal structure prediction tool accelerated by machine learning.pdf