2024/07/01 by Daniel Julian, Julian, Daniel, Rian Koots +3
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #Diatoms and Algae Research #FOS: Physical sciences #Genomics and Phylogenetic Studies #Hydrocarbon exploration and reservoir analysis
paper · pdf · doi:10.48550/arxiv.2407.01485
openalex publication_date 2024/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 → 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.