We train a range of model for the tmQMg dataset based on the Hückel SMILES. This was the method yielding the highest number of SMILES as well as the most general method since it requires neither DFT calculation and NBO analysis nor an initial SMILES representation. The LightGBM, Random Forest and FF-NN models are trained on the ECFP4 with a bit vector length of 1024. The GCNN and Gilmer models are trained on graph representations generated from information inherent in the SMILES. Finally, the Baseline, u-NatQ, d-NatQ models are trained on the NBO graph data from . Note that the u-NatQ and d-NatQ graphs are fed with node and edge information extracted from DFT calculations. Thus, when generating these graphs the target property (polarizability, HOMO-LUMO gap or dipole moment) is immediately available. In addition, the baseline, u-NatQ and d-NatQ graphs contain some 3D information as it has bond lengths as an edge feature from the optimized geometry. Contrary to this, the models based on SMILES only have the 2D graph defined by the SMILES, as well as the cheminformatics properties available through RDKit. All models are evaluated on the same test set as outlined in the computational methodology. We apply a naive approach where all remaining SMILES in the Hückel SMILES set are used for training. A comparison of the resulting R 2 correlation metrics are seen in Figure . Generally, there appears to be no difference among the methods when it comes to predicting polarizability. The polarizability is to a certain extent an additive property. As such, this correlation can be easily learned with simple ML models based on circular fingerprints with little to no gain in using more advanced models and/or molecular featurizations.