Crystal graph attention networks for the prediction of stable materials

Graph neural networks for crystal structures typically use the atomic positions and the atomic species as input. Unfortunately, this information is not available when predicting new materials, for which the precise geometrical information is unknown. We circumvent this problem by replacing the preci...

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Detalles Bibliográficos
Autores principales: Schmidt, Jonathan, Pettersson, Love, Verdozzi, Claudio, Botti, Silvana, Marques, Miguel A. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2021
Materias:
Physical and Materials Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641929/
https://www.ncbi.nlm.nih.gov/pubmed/34860548
http://dx.doi.org/10.1126/sciadv.abi7948

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641929/
https://www.ncbi.nlm.nih.gov/pubmed/34860548
http://dx.doi.org/10.1126/sciadv.abi7948

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