# Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns

https://mdr.nims.go.jp/datasets/889a61be-efe7-4d7e-aa39-f5dd7e70f80a

## File

- [Advanced Science - 2023 - Uryu - Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder.pdf](https://mdr.nims.go.jp/filesets/c732c707-3150-4384-bceb-53814db3ddba/download) ([Detail](https://mdr.nims.go.jp/filesets/c732c707-3150-4384-bceb-53814db3ddba.md))

## Id

889a61be-efe7-4d7e-aa39-f5dd7e70f80a

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-12-12T07:12:21.369283Z

## Updated at

2024-12-13T03:30:39.017919Z

## Published at

2024-12-13T03:30:39.109500Z

## Doi



## First published url

https://doi.org/10.1002/advs.202304546

## Date published

2023-11-14

## Recorded date published

2024-1

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase
    Powder Diffraction Patterns
  title_type: original
  lang: en

## Description

- description: Since the discovery of the quasicrystal, approximately 100 stable quasicrystalsare
    identified. To date, the existence of quasicrystals is verified usingtransmission
    electron microscopy; however, this technique requiressignificantly more elaboration
    than rapid and automatic powder X-raydiffraction. Therefore, to facilitate the
    search for novel quasicrystals,developing a rapid technique for phase-identification
    from powder diffractionpatterns is desirable. This paper reports the identification
    of a new Al–Si–Ruquasicrystal using deep learning technologies from multiphase
    powderpatterns, from which it is difficult to discriminate the presence ofquasicrystalline
    phases even for well-trained human experts. Deep neuralnetworks trained with artificially
    generated multiphase powder patternsdetermine the presence of quasicrystals with
    an accuracy >92% from actualpowder patterns. Specifically, 440 powder patterns
    are screened using thetrained classifier, from which the Al–Si–Ru quasicrystal
    is identified. Thisstudy demonstrates an excellent potential of deep learning to
    identify anunknown phase of a targeted structure from powder patterns even whenexisting
    in a multiphase sample.
  description_type: abstract
  lang: en

## Creator

- name: Hirotaka Uryu
  role: author
- name: Tsunetomo Yamada
  role: author
- name: Koichi Kitahara
  role: author
- name: Alok Singh
  role: author
  orcid: https://orcid.org/0000-0001-5515-8305
  organization: National Institute for Materials Science
- name: Yutaka Iwasaki
  role: author
  orcid: https://orcid.org/0000-0002-7317-4939
  organization: National Institute for Materials Science
- name: Kaoru Kimura
  role: author
  orcid: https://orcid.org/0000-0001-5050-4256
  organization: National Institute for Materials Science
- name: Kanta Hiroki
  role: author
- name: Naoki Miyao
  role: author
- name: Asuka Ishikawa
  role: author
- name: Ryuji Tamura
  role: author
- name: Satoshi Ohhashi
  role: author
- name: Chang Liu
  role: author
- name: Ryo Yoshida
  role: author

## Contact agent



## Publisher

organization: Wiley

## Managing organization



## Keyword

- subject: deep learning
  schema: not_defined
- subject: x-ray powder diffraction
  schema: not_defined
- subject: quasicrystal
  schema: not_defined
- subject: phase identification
  schema: not_defined
- subject: machine learning
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Advanced Science
  issn: '21983844'
  volume: '11'
  issue: '1'
  article_number: '2304546'

## Conference



## Related item



## Funding

- identifier: 19H05820
  funder_name: Japan Society for the Promotion of Science
- identifier: 19H05818
  funder_name: Japan Society for the Promotion of Science
- identifier: JPMJCR19I3
  funder_name: Core Research for Evolutional Science and Technology
- identifier: JPMJCR22O3
  funder_name: Core Research for Evolutional Science and Technology

## 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



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## Fileset

- id: c732c707-3150-4384-bceb-53814db3ddba
  filename: Advanced Science - 2023 - Uryu - Deep Learning Enables Rapid Identification
    of a New Quasicrystal from Multiphase Powder.pdf
  content_type: application/pdf
  size: 1914251
  md5: 6c08e15193e9fb40c4cfa0d28c348b89

## Thumbnail

fileset_id: c732c707-3150-4384-bceb-53814db3ddba
filename: Advanced Science - 2023 - Uryu - Deep Learning Enables Rapid Identification
  of a New Quasicrystal from Multiphase Powder.pdf