simulations. For the prediction of charges on this dataset, the full vector dipole moment was used to train the models. To compare the CM5 charges, DFT calculations on the QM9 dataset were performed using the same basis set and functional, i.e. B3LYP/6-31G(2df,p), previously. It is found that PiNet2-dipole outperforms PiNet-dipole in all cases for the QM9 dataset with a performance gain of 10% for the AC model as shown in Fig. in the Supporting Information. However, this advantage brought by the vectorial features diminishes and becomes marginal when a larger network size was used (Fig. in the Supporting Information). When comparing different flavors of models implemented in PiNet2-dipole, AC model shows an MAE of 0.015 D while the AD and AC+BC(R) slightly improve this result with MAEs of 0.014 D and 0.013 D respectively. Therefore, we will focus on the AC model in the following discussion of the QM9 dataset.