For the dataset, different singly and doubly substituted NBD/QC systems are selected to get an even coverage of chemical space where every small ligand (Figure ) is represented at every position on the phenyl rings and NBD scaffold a roughly equal number of times. The singly substituted NBD/QC systems include all directly attached EWGs and EDGs as well as phenyl and phenylacetylene groups with all combinations of up to three EWGs and EDGs in the ortho-, meta-, and para-position (resulting in a total of 18 3 • 2 + 18 = 11, 682 singly substituted systems). The doubly substituted NBD/QC systems (substituted in position 1 and 2, 1 and 3, or 1 and 4, see Figure ) included all combinations of the EWGs, EDGs, and single ortho-, meta-, or para-substituted phenyl and phenylacetylene groups (resulting in a total of ((17•3+1)•2+17)+2-1 2 • 3 = 22, 143 double substituted systems). Hence, the dataset consisted of 33,825 unique NBD/QC systems. Of these, 119 of the GFN2-xTB calculations failed and are omitted from the dataset. This results in a total of 33,706 NBD/QC systems, which are split 80/20 for training and test set, respectively. A 5-fold cross-validation is used to train the different GCNs for 100 epochs with a batch size of 512 and using the Adam optimizer with a learning rate of 0.01 on a MSE loss (see learning curves in supporting information). To prevent overfitting, the trained GCN with the overall best validation loss for each property are saved. Figure shows the GCN-predicted vs GFN2-xTB values for the storage energy, absorption, and TBR barriers for the test set. The respective mean absolute errors are 1.66±2.46 kJ/mol, 8.62±10.41 nm, and 7.62±11.17 kJ/mol. In the case of the TBR barriers the most significant deviations are for molecules with low barriers, meaning that the GCN predictions could lead to a significant number of false positives with respect to the barrier. However, such false positives can be efficiently eliminated by subsequent GFN2-xTB calculations and does not present a significant problem.