Quasicrystals predicted and discovered by machine learning

Chang Liu; Koichi Kitahara; Asuka Ishikawa; Takanobu Hiroto; Alok Singh; Erina Fujita; Yukari Katsura; Yuki Inada; Ryuji Tamura; Kaoru Kimura; Ryo Yoshida

doi:10.1103/physrevmaterials.7.093805

論文 Quasicrystals predicted and discovered by machine learning

Chang Liu ; Koichi Kitahara ; Asuka Ishikawa ; Takanobu Hiroto (National Institute for Materials Science) ; Alok Singh (National Institute for Materials Science) ; Erina Fujita (National Institute for Materials Science) ; Yukari Katsura (National Institute for Materials Science) ; Yuki Inada ; Ryuji Tamura ; Kaoru Kimura (National Institute for Materials Science) ; Ryo Yoshida

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引用

Chang Liu, Koichi Kitahara, Asuka Ishikawa, Takanobu Hiroto, Alok Singh, Erina Fujita, Yukari Katsura, Yuki Inada, Ryuji Tamura, Kaoru Kimura, Ryo Yoshida. Quasicrystals predicted and discovered by machine learning. Physical Review Materials. 2023, 7 (9), 093805. https://doi.org/10.1103/physrevmaterials.7.093805

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(abstract)

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.

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