Table : Mean absolute errors (MAEs) and root mean squared errors (RMSEs) of SpaiNN models for ethylene, propylene, and butene using SchNet or PaiNN representation, respectively. The respective models were trained on the energies (E j ) and forces (F j ) of the three lowest singlet states (S 0 , S 1 , and S 2 ) and the non-adiabatic coupling vectors (C ij ) between these states. The errors are reported for each individual state or coupling and the averaged errors. The errors of PaiNN-based models are highlighted in gray and the lowest error when comparing PaiNN-and SchNet-based models are indicated by bold numbers. To account for trade-offs between the target properties, a weight-factor of 1.0 was employed for energies, forces and non-adiabatic couplings, while a factor of 0.1 was used for the dipoles. Noticeably, the NACs show a decrease, when the corresponding energies are close to each other, which might be an effect due to the Berry phase or the Born-Oppenheimer breakdown and is thus considered an artifact. This effect is corrected for with ML models. 2 upon excitation into S 2 (b) as derived from SHARC simulations spanning 100 fs (with a time-step of 0.5 fs). The solid lines represent outcomes from 100 SHARC trajectories on SA(3)-CASSCF(2,2)/cc-pVDZ (a) or MR-CISD/aug-cc-pVDZ level of theory, respectively. 9,10 Dashed lines reflect results from 600 (a) or 3846 SHARC trajectories (b), incorporating non-adiabatic couplings, energies, and forces predicted by a SpaiNN model leveraging SchNet representation. The background color is white in regions where SpaiNN models yield reasonable results, as determined by the total energy of the molecules at each time-step, while other regions are shaded gray. Kinetic rate constants for a sequential kinetics scheme were derived using KiMoPack and are presented alongside the population plots. Bold numbers denote the excited-state lifetimes as obtained from reference calculations, the ML lifetimes are given in parenthesis.