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Chemical modelling serves two purposes in dynamical models: accounting forthe effect of microphysics on the dynamics and providing observable signatures.Ideally, the former must be done as part of the hydrodynamic simulation butthis comes with a prohibitive computational cost which leads to manysimplifications being used in practice. To produce a statistical emulator thatreplicates a full chemical model capable of solving the temperature andabundances of a gas through time. This emulator should suffer only a minor lossof accuracy over including a full chemical solver in a dynamical model butwould have a fraction of the computational cost. The gas-grain chemical codeUCLCHEM was updated to include heating and cooling processes and a largedataset of model outputs from possible starting conditions was produced. Aneural 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 stableover 1000 iterations with an MSE of 0.003 the log scaled temperature after onetime step and 0.006 after 1000 time steps. Chemulator was found to beapproximately 50,000 times faster than the time dependent model it emulates butcan introduce a significant error to some models.

Chemulator: Fast, accurate thermochemistry for dynamical models through emulation