This work constructs a surrogate model for QCT calculations by leveraging a small subset of reaction rate coefficients evaluated over a limited temperature range. Our objective is to accurately and efficiently obtain inelastic, exchange, and dissociation rate coefficients as functions of translational temperature and a reduced set of physical properties of the diatomic molecule under consideration. This section discusses (i) the surrogate architectures; (ii) the construction of the training dataset, generated by running quasi-classical trajectories from 90 sampled initial states for five translational temperatures; (iii) the employed sampling algorithm, which relies on a combination of the dissociation-group-based sampling proposed by Venturi et al. and a novel active-sampling method based on the excitation dynamics.