  Astrochemical models are important tools to interpret observations of
molecular and atomic species in different environments. However, these models
are time-consuming, precluding a thorough exploration of the parameter space,
leading to uncertainties and biased results. Using neural networks to simulate
the behavior of astrochemical models is a way to circumvent this problem,
providing fast calculations that are based on real astrochemical models. In
this paper, we present a fast neural emulator of the astrochemical code
Nautilus based on conditional neural fields. The resulting model produces the
abundance of 192 species for arbitrary times between 1 and 10$^7$ years.
Uncertainties well below 0.2 dex are found for all species, while the computing
time is of the order of 10$^4$ smaller than Nautilus. This will open up the
possibility of performing much more complex forward models to better understand
the physical properties of the interstellar medium. As an example of the power
of these models, we ran a feature importance analysis on the electron abundance
predicted by Nautilus. We found that the electron density is coupled to the
initial sulphur abundance in a low density gas. Increasing the initial sulphur
abundance from a depleted scenario to the cosmic abundance leads to an
enhancement of an order of magnitude of the electron density. This enhancement
can potentially influence the dynamics of the gas in star formation sites.
