most citedPhysical Pooling Functions in Graph Neural Networks for Molecular Property Prediction

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cs.LG2022★ 59 cited

Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction

Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber +4

Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the…