  In order to understand grain-surface chemistry, one must have a good
understanding of the reaction rate parameters. For diffusion-based reactions,
these parameters are binding energies of the reacting species. However,
attempts to estimate these values from grain-surface abundances using Bayesian
inference are inhibited by a lack of enough sufficiently constraining data. In
this work, we use the Massive Optimised Parameter Estimation and Data (MOPED)
compression algorithm to determine which species should be prioritised for
future ice observations to better constrain molecular binding energies. Using
the results from this algorithm, we make recommendations for which species
future observations should focus on.
