Given the challenge of fitting NACs, we further evaluate the predictive capability of SpaiNN models trained on XALKENEDB and reference data for CH 2 NH + 2 by conducting inference on unseen data covering two degrees of freedom. In the first dimension, we varied dihedral angles around the sp 2 double bond, specifically δ(HC = CH) for C 2 H 4 , C 3 H 6 , and C 4 H 8 , or δ(HC = NH) for CH 2 NH + 2 , within the range of 0 to 180 • . In the second dimension, we scanned the length of the respective sp 2 double bond, ranging between 2.0 and 3.0 Bohr for the alkenes, and between 2.4 and 4.4 Bohr for CH 2 NH + 2 . To assess the accuracy of the NAC predictions across the geometries on the two-dimensional grids, we compare the prediction errors obtained for SpaiNN models employing SchNet or PaiNN representations. First, we compute the logarithmic, absolute error between each of the models and the quantum chemical reference (∆C ij , cf. Figure and). Subsequently, we subtract the SpaiNN(SchNet) from the SpaiNN(PaiNN) prediction errors, yielding ∆∆C ij = ∆C ij (PaiNN)-∆C ij (SchNet) values for every coupling (between states i and j) at every point on the 2D grid. The respective distribution of these error differences on the 2D grid is shown for ∆∆C 01 of C 2 H 4 , C 3 H 6 , and C 4 H 8 in Figure i, and for ∆∆C12 and ∆∆C 01 of CH 2 NH + 2 in Figure ). The respective error-difference plots for all NACs, potential energies, forces, or dipole moment vectors on the same grid are compiled in Figures and.