Machine Learning to Predict Quasicrystals from Chemical Compositions.

Liu, Chang; Fujita, Erina; Katsura, Yukari; Inada, Yuki; Ishikawa, Asuka; Tamura, Ryuji; Kimura, Kaoru; Yoshida, Ryo

Liu, Chang; Fujita, Erina; Katsura, Yukari; Inada, Yuki; Ishikawa, Asuka; Tamura, Ryuji; Kimura, Kaoru; Yoshida, Ryo.

Afiliação

Liu C; The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, 190-8562, Japan.
Fujita E; Department of Advanced Materials Science, The University of Tokyo, Kashiwa, 277-8561, Japan.
Katsura Y; Department of Advanced Materials Science, The University of Tokyo, Kashiwa, 277-8561, Japan.
Inada Y; Department of Advanced Materials Science, The University of Tokyo, Kashiwa, 277-8561, Japan.
Ishikawa A; Department of Materials Science and Technology, Tokyo University of Science, Tokyo, 125-8585, Japan.
Tamura R; Department of Materials Science and Technology, Tokyo University of Science, Tokyo, 125-8585, Japan.
Kimura K; Department of Advanced Materials Science, The University of Tokyo, Kashiwa, 277-8561, Japan.
Yoshida R; The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, 190-8562, Japan.

Adv Mater ; 33(36): e2102507, 2021 Sep.

Article em En | MEDLINE | ID: mdl-34278631

Palavras-chave

approximant crystals; high-throughput screening; machine learning; materials informatics; quasicrystals

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Adv Mater Ano de publicação: 2021 Tipo de documento: Article

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