Graph neural networks for materials science and chemistry. (arXiv:2208.09481v1 [physics.chem-ph])

Machine learning plays an increasingly important role in many areas of
chemistry and materials science, e.g. to predict materials properties, to
accelerate simulations, to design new materials, and to 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 …

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