Benchmarking graph neural networks for materials chemistry
Abstract Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to elect...
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Autores principales: | , , , |
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Formato: | article |
Lenguaje: | EN |
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Nature Portfolio
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/86b6e1b70bf24df7bb74a248da2c8e25 |
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