# Quasicrystals predicted and discovered by machine learning

https://mdr.nims.go.jp/datasets/c8d96bad-2426-4c69-9f31-3b1f48af6e4e

## File

- [PhysRevMaterials.7.093805.pdf](https://mdr.nims.go.jp/filesets/cacefc04-cbdf-4f46-a2f1-90960c44d60c/download) ([Detail](https://mdr.nims.go.jp/filesets/cacefc04-cbdf-4f46-a2f1-90960c44d60c.md))

## Id

c8d96bad-2426-4c69-9f31-3b1f48af6e4e

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-11-14T09:17:54.907925Z

## Updated at

2024-11-21T07:36:03.413182Z

## Published at

2024-11-21T07:36:03.467429Z

## Doi



## First published url

https://doi.org/10.1103/physrevmaterials.7.093805

## Date published

2023-09-25

## Recorded date published

2023-9

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Quasicrystals predicted and discovered by machine learning
  title_type: original
  lang: en

## Description

- description: "Quasicrystals represent a class of ordered materials that have diffraction
    symmetry forbidden in periodic\r\ncrystals. Since the first discovery of quasicrystals
    in 1984, approximately 100 thermodynamically stable quasicrystals have been synthesized.
    The discovery of new quasicrystals has led to the observation of novel physical
    phenomena, such as robust quantum criticality, fractal superconductivity, and
    peculiar long-range magnetic ordering. However, the pace of discovery of new quasicrystals
    has significantly slowed down, which is attributed to the lack of design principles
    for exploring new quasicrystals. Here, we demonstrate that machine learning can
    greatly accelerate the process of material discovery. Our model can predict stable
    quasicrystalline phases with high accuracy. With this model, we discovered three
    stable decagonal quasicrystals through an exhaustive screening of more than 1000
    ternary aluminum alloy systems."
  description_type: abstract
  lang: und

## Creator

- name: Chang Liu
  role: author
- name: Koichi Kitahara
  role: author
- name: Asuka Ishikawa
  role: author
- name: Takanobu Hiroto
  role: author
  orcid: https://orcid.org/0000-0002-6176-5782
  organization: National Institute for Materials Science
- name: Alok Singh
  role: author
  orcid: https://orcid.org/0000-0001-5515-8305
  organization: National Institute for Materials Science
- name: Erina Fujita
  role: author
  orcid: https://orcid.org/0000-0002-0987-5597
  organization: National Institute for Materials Science
- name: Yukari Katsura
  role: author
  orcid: https://orcid.org/0000-0002-8905-2995
  organization: National Institute for Materials Science
- name: Yuki Inada
  role: author
- name: Ryuji Tamura
  role: author
- name: Kaoru Kimura
  role: author
  orcid: https://orcid.org/0000-0001-5050-4256
  organization: National Institute for Materials Science
- name: Ryo Yoshida
  role: author
  orcid: https://orcid.org/0000-0001-8092-0162

## Contact agent



## Publisher

organization: American Physical Society (APS)

## Managing organization



## Keyword

- subject: Crystal forms
  schema: not_defined
- subject: Crystal phenomena
  schema: not_defined
- subject: Crystal structure
  schema: not_defined
- subject: Alloys
  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: Physical Review Materials
  issn: '24759953'
  volume: '7'
  issue: '9'
  article_number: '093805'

## Conference



## Related item



## Funding

- identifier: 19H05820
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: 19H05818
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JPMJCR22O3
  funder_name: Japan Science and Technology Agency
- identifier: JPMJCR19I3
  funder_name: Japan Science and Technology Agency

## Instrument



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## Measurement method



## Specimen



## Chemical composition



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## Fileset

- id: cacefc04-cbdf-4f46-a2f1-90960c44d60c
  filename: PhysRevMaterials.7.093805.pdf
  content_type: application/pdf
  size: 5549662
  md5: feff49016b159306fc3902de427cbf03

## Thumbnail

fileset_id: cacefc04-cbdf-4f46-a2f1-90960c44d60c
filename: PhysRevMaterials.7.093805.pdf