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