  Machine learning-based models to predict product state distributions from a
distribution of reactant conditions for atom-diatom collisions are presented
and quantitatively tested. The models are based on function-, kernel- and
grid-based representations of the reactant and product state distributions.
While all three methods predict final state distributions from explicit
quasi-classical trajectory simulations with R$^2$ > 0.998, the grid-based
approach performs best. Although a function-based approach is found to be more
than two times better in computational performance, the kernel- and grid-based
approaches are preferred in terms of prediction accuracy, practicability and
generality. The function-based approach also suffers from lacking a general set
of model functions. Applications of the grid-based approach to nonequilibrium,
multi-temperature initial state distributions are presented, a situation common
to energy distributions in hypersonic flows. The role of such models in Direct
Simulation Monte Carlo and computational fluid dynamics simulations is also
discussed.
