3.2 Graph neural networks GNN models learn properties directly from the molecular structure and have shown high prediction accuracies for a variety of both pure component and mixture properties . Each molecule is represented as a graph with atoms as nodes and bonds as edges with corresponding feature vectors that contain atom and bond information, respectively. GNN models learn to extract local structural information about the molecular graph in graph convolutions that are then encoded into a vector representation. This molecular vector is then mapped to the property of interest by using a feedforward neural network. For a detailed description of GNN models, we refer the interested reader to overviews in Refs. .