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

amorphous oxides fracture machine learning molecular dynamics simulation nanoductility

Journal

ACS nano
ISSN: 1936-086X
Titre abrégé: ACS Nano
Pays: United States
ID NLM: 101313589

Informations de publication

Date de publication:
23 Nov 2021
Historique:
pubmed: 2 11 2021
medline: 2 11 2021
entrez: 1 11 2021
Statut: ppublish

Résumé

Thin films of amorphous alumina (a-Al

Identifiants

DOI: 10.1021/acsnano.1c05619 PMID: 34723489
pubmed: 34723489
doi: 10.1021/acsnano.1c05619
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

17705-17716

Auteurs

Tao Du (T)

Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark.

Han 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.
ORCID: 0000-0002-4899-9998

Longwen 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.

Søren S Sørensen (SS)

Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark.
ORCID: 0000-0003-2230-7823

Mathieu 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.
ORCID: 0000-0003-4600-0631

Morten M Smedskjaer (MM)

Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark.
ORCID: 0000-0003-0476-2021

Classifications MeSH