Prediction of viscosity behavior in oxide glass materials using cation fingerprints with artificial neural networks.

Hwang, Jaekyun; Tanaka, Yuta; Ishino, Seiichiro; Watanabe, Satoshi

Hwang, Jaekyun; Tanaka, Yuta; Ishino, Seiichiro; Watanabe, Satoshi.

Afiliação

Hwang J; Department of Materials Engineering, The University of Tokyo, Tokyo, Japan.
Tanaka Y; Department of Physics, The University of Tokyo, Tokyo, Japan.
Ishino S; Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
Watanabe S; Department of Materials Engineering, The University of Tokyo, Tokyo, Japan.

Sci Technol Adv Mater ; 21(1): 492-504, 2020 Jul 22.

Article em En | MEDLINE | ID: mdl-32939174

Palavras-chave

107 Glass and ceramic materials; 404 Materials informatics/Genomics; 60 New topics/Others; Chemical composition; glass; machine learning; materials informatics; neural network; oxide; viscosity

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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: Sci Technol Adv Mater Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Japão

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