Predicting excited state properties presents a challenge due to the need to consider multiple electronic states and their associated couplings (Q ij ), such as NACs (C ij ) or transition dipole moments (µ ij ). The prediction of these properties, that arise from two electronic wave functions has proven to be difficult when utilizing ML techniques. This is because the phase of each coupling property depends on the sign of the wave functions of the electronic states i and j, that are coupled by this property, which however is not uniquely defined in quantum chemical calculations. Consequently, there can be random phase jumps in the coupling values along a reaction path. Thus, the couplings cannot be learned directly.