59 citations · 186 across the 4 of their papers we have counts for
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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…
cs.LG2022★ 36 cited
Graph Machine Learning for Design of High-Octane Fuels
Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann +7
Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition p…