Deep Graph Learning for Molecules and Materials

Abstract

Machine learning approaches have become an important tool in chemistry and materials science for the accurate and efficient prediction of physical properties. Most notably among them are graph neural networks that leverage the inherent graph structure of molecules and materials in order to achieve state-of-the-art accuracy. In this perspective we give a brief introduction to the theoretical foundations of graph neural networks for molecular structures and their specific applications in chemistry and materials science. We conclude with a short outlook discussing remaining research questions as well as opportunities for further developments of the field.