Predicting energy and stability of known and hypothetical crystals using graph neural network

The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerated scree...

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Detalles Bibliográficos
Autores principales: Pandey, Shubham, Qu, Jiaxing, Stevanović, Vladan, St. John, Peter, Gorai, Prashun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8600245/
https://www.ncbi.nlm.nih.gov/pubmed/34820646
http://dx.doi.org/10.1016/j.patter.2021.100361

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8600245/
https://www.ncbi.nlm.nih.gov/pubmed/34820646
http://dx.doi.org/10.1016/j.patter.2021.100361

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