Graph neural networks for materials science and chemistry

        Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.


DOI: 10.1038/s43246-022-00315-6

Research Groups: SIMPLAIX

Publication type: Journal

Journal: Communications Materials

Citation: Commun Mater 3(1),93

Date Published: 1st Dec 2022

Registered Mode: by DOI

Authors: Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich

Reiser, P., Neubert, M., Eberhard, A., Torresi, L., Zhou, C., Shao, C., Metni, H., van Hoesel, C., Schopmans, H., Sommer, T., & Friederich, P. (2022). Graph neural networks for materials science and chemistry. In Communications Materials (Vol. 3, Issue 1). Springer Science and Business Media LLC.

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Created: 4th Aug 2023 at 09:59

Last updated: 5th Mar 2024 at 21:25

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