Understanding these photochemical reactions computationally requires the accurate description of internal conversion between electronic states, particularly accessing NACs at regions of the conical intersections. These intersections are characterized by energetic degeneracy between electronic states, enabling ultrafast, non-radiative relaxation pathways. Consequently, conical intersections serve as critical junctions for light-induced reactions and processes that decide which state is populated. Herein, we evaluate the predictive capability of SpaiNN in determining equivariant, vectorial properties, namely dipole moments (µ µ µ ij ) and NACs (C ij ). The discussion follows a conventional ap-proach in understanding photophysical processes and reactions through computational chemistry. Initially, we focus on static considerations, including the prediction of absorption spectra (Franck-Condon region) and properties of crossing geometries (region of conical intersections). Subsequently, we showcase the application of SpaiNN predictions in conducting photodynamics simulations. 2 ) as reported earlier by some of us. For the three lowest singlet states of C 2 H 4 and C 3 H 6 , we trained models on energies (E j ) and forces (F j , j = {0, 1, 2}) as well as the NACs (C 01 , C 02 and C 12 ). Furthermore, we trained models on these nine features and additional dipole moment vectors, namely transition dipoles (µ µ µ 01 , µ µ µ 02 , µ µ µ 12 ) and permanent dipoles (µ µ µ j , j = {0, 1, 2}) for C 4 H 8 and CH 2 NH + 2 . Details on hyperparameters of the models are compiled in the supporting information (cf. section S3.1).