  This work shows that feed-forward neural networks can predict the final
ro-vibrational state distributions of inelastic and reactive processes of the
reaction of Ca $+$ H2 $\rightarrow$ CaH $+$ H in the hyperthermal regime,
relevant for buffer gas chemistry. Furthermore, these models can be extended to
the isotopologues of the reaction involving deuterium and tritium. In addition,
we develop a neural network model that can learn across the chemical space
based on the isotopologues of hydrogen. The model can predict the outcome of a
reaction whose reactants have never been seen. This is done by training on the
Ca $+$ H2 and Ca $+$ T2 reactions and subsequently predicting the Ca $+$ D2
reaction.
