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In order to obtain a good understanding of astrochemistry, it is crucial tobetter understand the key parameters that govern grain-surface chemistry. Formany chemical networks, these crucial parameters are the binding energies ofthe species. However, there exists much disagreement regarding these values inthe literature. In this work, a Bayesian inference approach is taken toestimate these values. It is found that this is difficult to do in the absenceof enough data. The Massive Optimised Parameter Estimation and Data (MOPED)compression algorithm is then used to help determine which species should beprioritised for future detections in order to better constrain the values ofbinding energies. Finally, an interpretable machine learning approach is takenin order to better understand the non-linear relationship between bindingenergies and the final abundances of specific species of interest.

A statistical and machine learning approach to the study of astrochemistry