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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Association for the Advancement of Science
2021
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| Materias: | |
| 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 |