Using Statistical Emulation and Knowledge of Grain-Surface Diffusion for
  Bayesian Inference of Reaction Rate Parameters: An Application to a Glycine
  Network
  There exists much uncertainty surrounding interstellar grain-surface
chemistry. One of the major reaction mechanisms is grain-surface diffusion for
which the the binding energy parameter for each species needs to be known.
However, these values vary significantly across the literature which can lead
to debate as to whether or not a particular reaction takes place via diffusion.
In this work we employ Bayesian inference to use available ice abundances to
estimate the reaction rates of the reactions in a chemical network that
produces glycine. Using this we estimate the binding energy of a variety of
important species in the network, by assuming that the reactions take place via
diffusion. We use our understanding of the diffusion mechanism to reduce the
dimensionality of the inference problem from 49 to 14, by demonstrating that
reactions can be separated into classes. This dimensionality reduction makes
the problem computationally feasible. A neural network statistical emulator is
used to also help accelerate the Bayesian inference process substantially.
  The binding energies of most of the diffusive species of interest are found
to match some of the disparate literature values, with the exceptions of atomic
and diatomic hydrogen. The discrepancies with these two species are related to
limitations of the physical and chemical model. However, the use of a dummy
reaction of the form H + X -> HX is found to somewhat reduce the discrepancy
with the binding energy of atomic hydrogen. Using the inferred binding energies
in the full gas-grain version of UCLCHEM results in almost all the molecular
abundances being recovered.
