Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning-Reference-Cited by-同舟云学术

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

Published:2021-11-01 Issue:11 Volume:15 Page:17705-17716
ISSN:1936-0851
Container-title:ACS Nano
language:en
Short-container-title:ACS Nano

Author:

Du Tao¹,Liu Han²^ORCID,Tang Longwen²,Sørensen Søren S.¹^ORCID,Bauchy Mathieu²^ORCID,Smedskjaer Morten M.¹^ORCID

Affiliation:

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

2. Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, California 90095, United States

Funder

H2020 Marie Sk??odowska-Curie Actions

Villum Fonden

National Science Foundation

Publisher

American Chemical Society (ACS)

Subject

General Physics and Astronomy,General Engineering,General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsnano.1c05619

Reference59 articles.

1. Microfracture beneath point indentations in brittle solids

2. VI. The phenomena of rupture and flow in solids

3. Indentation deformation and cracking in oxide glass –toward understanding of crack nucleation

4. Towards Ultrastrong Glasses

5. Failure of metals I: Brittle and ductile fracture

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