Machine learning of twin/matrix interfaces from local stress field-Reference-Cited by-同舟云学术

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Machine learning of twin/matrix interfaces from local stress field

Published:2023-09 Issue: Volume:228 Page:112322
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Troncoso Javier F.,Hu Yang^ORCID,della Ventura Nicolò M.^ORCID,Sharma Amit,Maeder Xavier,Turlo Vladyslav^ORCID

Funder

National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials

Centro Svizzero di Calcolo Scientifico

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

NCCR Catalysis

Publisher

Elsevier BV

Subject

Computational Mathematics,General Physics and Astronomy,Mechanics of Materials,General Materials Science,General Chemistry,General Computer Science

Reference91 articles.

1. Effective values of critical resolved shear stress for slip in polycrystalline magnesium and other HCP metals;Hutchinson;Scr. Mater.,2010

2. Measuring the critical resolved shear stresses in Mg alloys by instrumented nanoindentation;Sánchez-Martín;Acta Mater.,2014

3. Prismatic slip in magnesium;Stricker;J. Phys. Chem. C,2020

4. Basal and non-basal dislocation slip in Mg–Y;Sandlöbes;Mater. Sci. Eng. A,2013

5. Analysis of double cross-slip of pyramidal I screw dislocations and implications for ductility in Mg alloys;Ahmad;Acta Mater.,2020

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