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. 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 further development.

Automated summary

This article reviews the importance of machine learning in predicting materials properties, accelerating simulations, designing new structures, and predicting synthesis routes in chemistry and materials science. It specifically focuses on the growing field of graph neural networks (GNNs), which directly access the structural representation of molecules and materials. The review provides an overview of GNNs' basic principles, widely used datasets, state-of-the-art architectures, and various applications in the field, concluding with a roadmap for further development and application of GNNs.