  The chemistry of an astrophysical environment is closely coupled to its
dynamics, the latter often found to be complex. Hence, to properly model these
environments a 3D context is necessary. However, solving chemical kinetics
within a 3D hydro simulation is computationally infeasible for a even a modest
parameter study. In order to develop a feasible 3D hydro-chemical simulation,
the classical chemical approach needs to be replaced by a faster alternative.
We present mace, a Machine learning Approach to Chemistry Emulation, as a
proof-of-concept work on emulating chemistry in a dynamical environment. Using
the context of AGB outflows, we have developed an architecture that combines
the use of an autoencoder (to reduce the dimensionality of the chemical
network) and a set of latent ordinary differential equations (that are solved
to perform the temporal evolution of the reduced features). Training this
architecture with an integrated scheme makes it possible to successfully
reproduce a full chemical pathway in a dynamical environment. mace outperforms
its classical analogue on average by a factor 26. Furthermore, its efficient
implementation in PyTorch results in a sub-linear scaling with respect to the
number of hydrodynamical simulation particles.
