Universal machine learning framework for defect predictions in zinc blende semiconductors.

Mannodi-Kanakkithodi, Arun; Xiang, Xiaofeng; Jacoby, Laura; Biegaj, Robert; Dunham, Scott T; Gamelin, Daniel R; Chan, Maria K Y

Mannodi-Kanakkithodi, Arun; Xiang, Xiaofeng; Jacoby, Laura; Biegaj, Robert; Dunham, Scott T; Gamelin, Daniel R; Chan, Maria K Y.

Afiliación

Mannodi-Kanakkithodi A; Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL 60439, USA.
Xiang X; School of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA.
Jacoby L; Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA 98195, USA.
Biegaj R; Department of Chemistry, University of Washington, Seattle, WA 98195, USA.
Dunham ST; Materials Science & Engineering, University of Washington, Seattle, WA 98195, USA.
Gamelin DR; Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA.
Chan MKY; Department of Chemistry, University of Washington, Seattle, WA 98195, USA.

Patterns (N Y) ; 3(3): 100450, 2022 Mar 11.

Article en En | MEDLINE | ID: mdl-35510195

Palabras clave

combinatorial screening; computational materials science; density functional theory; high-throughput data; machine learning; materials informatics; mid-gap states; point defects; semiconductors

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Patterns (N Y) Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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