Machine-learning models for atom-diatom reactions across isotopologues

This work shows that feed-forward neural networks can predict the final rovibrational state distributions of inelastic and reactive processes of the reaction of Ca+H2→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.