Given the limitations of static photochemistry approaches in potentially overlooking crucial geometries and pathways as well as relaxation mechanisms, time scales and quantum yields, we integrated SpaiNN with SHARC 3.0 . The latter forms the methodological foundation for dynamic approaches employing surface hopping NAMD. To showcase the performance of SpaiNN-assisted SHARC simulations, we employed the SpaiNN models of C 4 H 8 and CH 2 NH + 2 to conduct photodynamics simulations of the respective molecules. For the latter molecule, this has been used to assess the accuracy of NAMD methods earlier as the accurate description of its photodynamics that are governed by ultrafast internal conversions between different electronically excited states require accurate NACs. To execute ML-photodynamics we employed the SchNetand PaiNN-based SpaiNN models of C 4 H 8 and CH 2 NH + 2 trained on energies (E j ), forces (F j ), NACs (C ij ), and dipole moment vectors (µ µ µ ij ) as discussed in section 4.1 (cf. Table ). Note that µ µ µ ij were included and trained for completeness, although they remain inactive in the photodynamics simulations under discussion. Nevertheless, their potential application lies in the prediction of electronic absorption spectra , thereby facilitating the selection of initial conditions within the conventional framework of NAMD simulations as discussed above (cf. 2 ) in Figure and 5b, respectively. The corresponding results obtained with SchNet-based SpaiNN models are shown in Figure . For both molecules, it is evident from the population curves in Figure that the SpaiNN-driven SHARC simulations are able to reproduce the ultrafast transitions between the different electronic states as obtained from SHARC reference calculations (cf. computational details in section S2.4). This is reflected in the match between the dashed (SpaiNN/SHARC simulations) and the solid lines (electronic structure theory/SHARC simulations) in the white shaded regions in Figure .