Predicting grain boundary dislocation structures through multidimensional neural networks and high-throughput phase-field calculations-Reference-Cited by-同舟云学术

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Predicting grain boundary dislocation structures through multidimensional neural networks and high-throughput phase-field calculations

Published:2024-01 Issue: Volume:233 Page:112761
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Qiu Di^ORCID,Li Yongxiang

Publisher

Elsevier BV

Subject

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

Reference32 articles.

1. Partial secondary dislocations in germanium grain boundaries;Bacmann;Philos. Mag. A,2006

2. The observation of various grain boundary atomic structures in Au by high-resolution electron microscopy;Krakow;J. Mater. Res.,2011

3. Vapour phase growth and grain boundary structure of molybdenum disulphide atomic layers;Najmaei;Nat. Mater.,2013

4. Periodic segregation of solute atoms in fully coherent twin boundaries;Nie;Science,2013

5. Report of the Symposium on the Plastic Deformation of Crystalline Solids;Frank,1950

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