  Chemical modelling serves two purposes in dynamical models: accounting for
the effect of microphysics on the dynamics and providing observable signatures.
Ideally, the former must be done as part of the hydrodynamic simulation but
this comes with a prohibitive computational cost which leads to many
simplifications being used in practice. To produce a statistical emulator that
replicates a full chemical model capable of solving the temperature and
abundances of a gas through time. This emulator should suffer only a minor loss
of accuracy over including a full chemical solver in a dynamical model but
would have a fraction of the computational cost. The gas-grain chemical code
UCLCHEM was updated to include heating and cooling processes and a large
dataset of model outputs from possible starting conditions was produced. A
neural network was then trained to map directly from inputs to outputs.
Chemulator replicates the outputs of UCLCHEM with an overall mean squared error
(MSE) of 0.0002 for a single time step of 1000 yr and is shown to be stable
over 1000 iterations with an MSE of 0.003 the log scaled temperature after one
time step and 0.006 after 1000 time steps. Chemulator was found to be
approximately 50,000 times faster than the time dependent model it emulates but
can introduce a significant error to some models.
