The proposed methodology presents a computationally viable alternative to running exhaustive StS QCT calculations using only 1% of the whole energy level set for training. Only ninety energy states are selected for each of the five translational temperatures, and they are then used to train the surrogate. The levels are sampled based on a novel hybrid methodology, which is developed to cover the space of energy transfer and dissociation processes adequately. In fact, the first half of training states are sampled based on the centrifugal barrier-based grouping strategy, which is well suited for dissociation processes. The second half is recursively sampled based on the energy transfer reaction rates. We tested SurQCT for the O 2 + O system and found that the surrogate achieves 85% computational cost