The QM7b dataset 55 consists of polarizability tensors from 7211 organic molecules, here we use the polarizabilities calculated with the B3LYP functional and the d-Aug-cc-pVDZ basis set. This dataset was used to train the CRK and the polarizability tensor α based on Eq. 9. In this setup, any planar molecule would have a zero diagonal component in the polarizability tensor. Therefore, we measure the extent of molecules by their thickness which we define as the variance σ 2 z in position R z . By this definition, a molecule with bonds only in the xy-plane has σ 2 z = 0. The QM7b dataset is visualized in terms of molecular thickness in the upper panels of Figs. and. The lower panels of these plots show the prediction accuracy as a function of molecular thickness for PiNet-χ and PiNet2-χ respectively. PiNet2-χ outperforms PiNet-χ in all models except the EEMtype (also see Table , where PiNet2-χ does better than PiNet-χ when molecules within a cutoff thickness were excluded). Besides the prediction accuracy, the scaling behavior of polarizability is equally important. A well-known issue that EEM-type models suffer from is the superlinear scaling of the polariz-ability with molecular size. To investigate the scaling behavior of our models we predicted the polarizability of n-alkane and n-alkene chains. The predicted isotropic polarizability α iso is plotted versus the number of carbon atoms n C of n-alkanes in Figs. and and for n-alkenes in Figs. and. In the PiNet-χ prediction for the EEM-type model, we see a superlinear deviation from the expected linear behavior for n-alkanes when the molecular size is outside of the training set. However, for PiNet2-χ, EEM no longer overestimates the polarizability of nalkanes and the scaling behavior is linear even for structures well outside of the training set.