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Description:

Quasicrystals represent a class of ordered materials that have diffraction symmetry forbidden in periodic
crystals. 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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Crystal forms, Crystal phenomena, Crystal structure, Alloys, Machine learning

Date published: 2023-09-25

Publisher: American Physical Society (APS)

Journal:

• Physical Review Materials (ISSN: 24759953) vol. 7 issue. 9 093805

Funding:

• Ministry of Education, Culture, Sports, Science and Technology 19H05820
• Ministry of Education, Culture, Sports, Science and Technology 19H05818
• Japan Science and Technology Agency JPMJCR22O3
• Japan Science and Technology Agency JPMJCR19I3

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

MDR DOI:

First published URL: https://doi.org/10.1103/physrevmaterials.7.093805

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Updated at: 2024-11-21 16:36:03 +0900

Published on MDR: 2024-11-21 16:36:03 +0900