Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning.

Du, Tao; Liu, Han; Tang, Longwen; Sørensen, Søren S; Bauchy, Mathieu; Smedskjaer, Morten M

Du, Tao; Liu, Han; Tang, Longwen; Sørensen, Søren S; Bauchy, Mathieu; Smedskjaer, Morten M.

Afiliación

Du T; Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark.
Liu H; Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, United States.
Tang L; Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, United States.
Sørensen SS; Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark.
Bauchy M; Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, United States.
Smedskjaer MM; Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark.

ACS Nano ; 15(11): 17705-17716, 2021 Nov 23.

Article en En | MEDLINE | ID: mdl-34723489

Palabras clave

amorphous oxides; fracture; machine learning; molecular dynamics simulation; nanoductility

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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: ACS Nano Año: 2021 Tipo del documento: Article País de afiliación: Dinamarca Pais de publicación: Estados Unidos

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