In part, this can be explained by the smaller range of λ GFN1 values (see below). However, a fundamental difference between the two targets also exists: While we predict λ GFN1 on the basis of the corresponding neutral state molecular equilibrium structures, this does not hold for λ DFT . In, the latter case, the differing neutral state equilibrium geometries (between GFN1-xTB and DFT) further complicate the learning task.