  Chemical kinetics plays an important role in governing the thermal evolution
in reactive flows problems. The possible interactions between chemical species
increase drastically with the number of species considered in the system.
Various ways have been proposed before to simplify chemical networks with an
aim to reduce the computational complexity of the chemical network. These
techniques oftentimes require domain-knowledge experts to handcraftedly
identify important reaction pathways and possible simplifications. Here, we
propose a combination of autoencoder and neural ordinary differential equation
to model the temporal evolution of chemical kinetics in a reduced subspace. We
demonstrated that our model has achieved a close-to 10-fold speed-up compared
to commonly used astro-chemistry solver for a 9-species primordial network,
while maintaining 1 percent accuracy across a wide-range of density and
temperature.
