Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach.

Torres, Pol; Wu, Stephen; Ju, Shenghong; Liu, Chang; Tadano, Terumasa; Yoshida, Ryo; Shiomi, Junichiro

Torres, Pol; Wu, Stephen; Ju, Shenghong; Liu, Chang; Tadano, Terumasa; Yoshida, Ryo; Shiomi, Junichiro.

Afiliación

Torres P; Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, 113-8656, Japan.
Wu S; EURECAT, Technology Center of Catalonia, Applied Artificial Intelligence, 08290 Cerdanyola, Barcelona, Spain.
Ju S; Departament de Física, Universitat Autònoma de Barcelona (UAB), Campus de Bellaterra, 08193 Bellaterra, Barcelona, Spain.
Liu C; Research Organization of Information and Systems, The Institute of Statistical Mathematics (ISM), 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan.
Tadano T; Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, 113-8656, Japan.
Yoshida R; China-UK Low Carbon Collage, Shanghai Jiao Tong University, Shanghai 201306, People's Republic of China.
Shiomi J; Research Organization of Information and Systems, The Institute of Statistical Mathematics (ISM), 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan.

J Phys Condens Matter ; 34(13)2022 Jan 25.

Article en En | MEDLINE | ID: mdl-35008073

Palabras clave

first principles; machine learning; phonon hydrodynamics; semiconductors; thermal transport; thermoelectricity

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Screening_studies Idioma: En Revista: J Phys Condens Matter Asunto de la revista: BIOFISICA Año: 2022 Tipo del documento: Article País de afiliación: Japón

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