  In many astrophysical applications, the cost of solving a chemical network
represented by a system of ordinary differential equations (ODEs) grows
significantly with the size of the network, and can often represent a
significant computational bottleneck, particularly in coupled chemo-dynamical
models. Although standard numerical techniques and complex solutions tailored
to thermochemistry can somewhat reduce the cost, more recently, machine
learning algorithms have begun to attack this challenge via data-driven
dimensional reduction techniques. In this work, we present a new class of
methods that take advantage of machine learning techniques to reduce complex
data sets (autoencoders), the optimization of multi-parameter systems (standard
backpropagation), and the robustness of well-established ODE solvers to to
explicitly incorporate time-dependence. This new method allows us to find a
compressed and simplified version of a large chemical network in a
semi-automated fashion that can be solved with a standard ODE solver, while
also enabling interpretability of the compressed, latent network. As a proof of
concept, we tested the method on an astrophysically-relevant chemical network
with 29 species and 224 reactions, obtaining a reduced but representative
network with only 5 species and 12 reactions, and a x65 speed-up.
