we can now build ∆-learning models that further improve on the linear approach. As expected, the ∆-learning variant of K s (termed ∆K s ) indeed performs significantly better than both the linear and the baseline model, approaching an MAE of 30 meV at the largest training set size. The GFN1-xTB calculations required for obtaining λ GFN1 can also be exploited in a different way. One challenge for the structure-based models is the indirect relationship between the neutral GFN1-xTB geometry and λ DFT . We therefore also explored property-based models (termed K p ) which use frontier orbital energies and gaps, Fermi levels, total energies and vertical energy differences of the neutral and cationic system to construct a representation, as fully detailed in Table . The respective ∆K p model is actually slightly better than the corresponding structure-based model ∆K s , despite not including any structural information. Finally, a combined model incorporating the structural and property kernels (termed ∆K sp ), performs better still, reaching an MAE of 25 meV at the largest training set size.