Following the simulation protocol described above, appropriate data has to be fed in the database as training data for the fitting algorithm. To sample relevant data, 100 trajectories, whose initial geometry and velocity are based on a Wigner sampling algorithm, were propagated for 100 fs with a timestep of 0.5 fs. The initial state for the trajectories was chosen to be the second excited adiabatic state of the model, which corresponds primarily to the bright B 2u (ππ * ) state. Energies and gradients of each state at each point along the trajectories were stored in the database. After the simulations, points close to each other were deleted from the database. As the hopping probability is high in regions with small energy gaps (cf. Eq. 1), two different radii were introduced for points being close to each other. In regions, where the energy difference between two states is smaller than 0.5 eV, points closer than 0.25 in dimensionless normal mode coordinates (using the euclidean norm) were deleted from the database. In areas, where the surfaces are energetically well-separated (energy gap >0.5 eV), points closer than 0.75 (using the euclidean norm in the dimensionless normal mode coordinates) were deleted from the database. Following that procedure, the number of data sets in the database was reduced from 20100 to finally 6148 data sets, which corresponds to a reduction of almost 70%. Please note that the fraction of the data that is deleted in the cleaning process, may be considered as the first indicator whether the training data is sufficient. We assume that only a fraction of the complete conformational space is relevant for the simulation and needs to be sampled accurately. Using an equidistant grid, the number of points increases exponentially with the dimensionality. An adaptive sampling scheme may overcome that hurdle, as it samples only the important regions in the conformational space. The latter allows to treat high dimensional systems with a limited number of points.