Cargando…
Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations
The density and configurational changes of crystal dislocations during plastic deformation influence the mechanical properties of materials. These influences have become clearest in nanoscale experiments, in terms of strength, hardness and work hardening size effects in small volumes. The mechanical...
Autores principales: | Yang, Zijiang, Papanikolaou, Stefanos, Reid, Andrew C. E., Liao, Wei-keng, Choudhary, Alok N., Campbell, Carelyn, Agrawal, Ankit |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237459/ https://www.ncbi.nlm.nih.gov/pubmed/32427971 http://dx.doi.org/10.1038/s41598-020-65157-z |
Ejemplares similares
-
Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
por: Jha, Dipendra, et al.
Publicado: (2020) -
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
por: Jha, Dipendra, et al.
Publicado: (2019) -
Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
por: Gupta, Vishu, et al.
Publicado: (2021) -
MPpredictor: An
Artificial Intelligence-Driven Web
Tool for Composition-Based Material Property Prediction
por: Gupta, Vishu, et al.
Publicado: (2023) -
Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design
por: Mao, Yuwei, et al.
Publicado: (2022)