Deep learning approach for chemistry and processing history prediction from materials microstructure

Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ mic...

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Detalles Bibliográficos
Autores principales: Farizhandi, Amir Abbas Kazemzadeh, Betancourt, Omar, Mamivand, Mahmood
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927426/
https://www.ncbi.nlm.nih.gov/pubmed/35296736
http://dx.doi.org/10.1038/s41598-022-08484-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927426/
https://www.ncbi.nlm.nih.gov/pubmed/35296736
http://dx.doi.org/10.1038/s41598-022-08484-7

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