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: Victor Fung, Jiaxin Zhang, Eric Juarez, Bobby G. Sumpter
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/86b6e1b70bf24df7bb74a248da2c8e25
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