  In this era of exoplanet characterisation with JWST, the need for a fast
implementation of classical forward models to understand the chemical and
physical processes in exoplanet atmospheres is more important than ever.
Notably, the time-dependent ordinary differential equations to be solved by
chemical kinetics codes are very time-consuming to compute. In this study, we
focus on the implementation of neural networks to replace mathematical
frameworks in one-dimensional chemical kinetics codes. Using the gravity
profile, temperature-pressure profiles, initial mixing ratios, and stellar flux
of a sample of hot-Jupiters atmospheres as free parameters, the neural network
is built to predict the mixing ratio outputs in steady state. The architecture
of the network is composed of individual autoencoders for each input variable
to reduce the input dimensionality, which is then used as the input training
data for an LSTM-like neural network. Results show that the autoencoders for
the mixing ratios, stellar spectra, and pressure profiles are exceedingly
successful in encoding and decoding the data. Our results show that in 90% of
the cases, the fully trained model is able to predict the evolved mixing ratios
of the species in the hot-Jupiter atmosphere simulations. The fully trained
model is ~1000 times faster than the simulations done with the forward,
chemical kinetics model while making accurate predictions.
