Material symmetry recognition and property prediction accomplished by crystal capsule representation

Learning the global crystal symmetry and interpreting the equivariant information is crucial for accurately predicting material properties, yet remains to be fully accomplished by existing algorithms based on convolution networks. To overcome this challenge, here we develop a machine learning (ML) m...

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Detalles Bibliográficos
Autores principales: Liang, Chao, Rouzhahong, Yilimiranmu, Ye, Caiyuan, Li, Chong, Wang, Biao, Li, Huashan
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/PMC10457372/
https://www.ncbi.nlm.nih.gov/pubmed/37626032
http://dx.doi.org/10.1038/s41467-023-40756-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10457372/
https://www.ncbi.nlm.nih.gov/pubmed/37626032
http://dx.doi.org/10.1038/s41467-023-40756-2

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