As a first benchmark we trained kQEq models for predicting dipole moments of organic molecules. As reference data, the dipole moments of 7500 random molecules from the QM9 database were calculated at the PBE0/def2-TZVP level, using ORCA (data provided in the SI). This set spans a wide range of small to medium sized molecules containing the elements C, H, N, O and F. From these structures, we randomly selected a validation set (used to optimize the regularisation parameter σ) and a test set of 1000 molecules each. The training sets used below were drawn from the remaining 4000 molecules. Figure depicts the learning curves of different kQEq models, using a range of SOAP cutoffs. For comparison, we also fitted a conventional QEq model to the same data.