The second sampling strategy is based on a novel idea of actively adding training states based on updating the probability distribution resulting from all the already sampled initial levels. The driving concept is that initial states characterized by dynamics very different from each other are desirable for efficient yet effective training that aims to construct a surrogate with excellent generalization capabilities. If the rate coefficient K i j is high, the states i and j are likely to be found in equilibrium between each other (i.e., j equilibrates very quickly to any change of i), and their dynamics are, therefore, very similar. Consequently, the QCT data obtained by considering i and j as initial states would be almost redundant. From this reasoning, we conclude that an optimal candidate would be a new state, j, that is characterized by very small K i j from each of the levels, i, already in the batch of training data. Initially, the three levels that mark the extremes of the effective diatomic potential are selected as starting seeds for the strategy. These consist of: i) the rovibrational ground level, ii) the dissociation limit (i.e., the state characterized by the largest outer turning point), and iii) the level with the highest rovibrational energy. After QCT calculations are completed for these three initial states, a probability distribution is defined, as shown in step 5 of Algorithm Update probability distribution function, p, as q = 1/ max (k i j , k min ), p = q/ ∑ q 6: