SchNet was used as provided in Ref. 57 with default hyperparameters, and trained only on QM9 tasks. It takes as input the nuclear charges and coordinates of each atom in a molecule which are calculated using quantum chemistry and provided along with the QM9 dataset. The atomic representations of each atom are refined using continuous-filter convolutional layers, thus taking into account other atoms in the molecule based on their relative distance. The atomic representations are then utilized to compute atomic contributions to the overall target, which are subsequently averaged or summed up to the total molecular target value. Since SchNet does not directly support ensembling, models with different initialization seeds were trained manually and their predictions averaged for each datapoint in the test set.