Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric enco...

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Detalles Bibliográficos
Autores principales: Chapman, James, Hsu, Tim, Chen, Xiao, Heo, Tae Wook, Wood, Brandon C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328988/
https://www.ncbi.nlm.nih.gov/pubmed/37419927
http://dx.doi.org/10.1038/s41467-023-39755-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328988/
https://www.ncbi.nlm.nih.gov/pubmed/37419927
http://dx.doi.org/10.1038/s41467-023-39755-0

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