All kQEq models clearly outperform the conventional QEq approach, underscoring the benefit of the additional flexibility obtained by using environment dependent electronegativities. Furthermore, it can be seen that the kQEq models improve continuously when given more data, whereas the MAE of the conventional approach quickly saturates. The model with the smallest SOAP cutoff used here (1.7 Å) shows the best performance for small training sets but stops improving when training on larger sets. Meanwhile, the larger cutoffs we tested (2.6 Å and 3.5 Å) continuously improve and reach an excellent accuracy of 0.15 D (compared to an intrinsic standard deviation of ca. 2 D).