Graph neural networks for materials science and chemistry

Graph neural networks for materials science and chemistry

Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and chemistry, indicating a possible road-map for their...

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Bibliographic Details
Main Authors: Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich
Format: Article
Language:English
Published: Nature Portfolio 2022-11-01
Series:Communications Materials
Online Access:https://doi.org/10.1038/s43246-022-00315-6

Internet

https://doi.org/10.1038/s43246-022-00315-6

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