  Databases of gas and surface chemical reactions are a key tool for scientists
working in a wide range of physical sciences. In Astrochemistry, databases of
chemical reactions are used as inputs to chemical models to determine the
abundances of the interstellar medium. Gas chemistry and, in particular, grain
surface chemistry and its treatment in gas-grain chemical models are however
areas of large uncertainty. Many reactions - especially on the dust grains -
have not been systematically experimentally studied. Moreover, experimental
measurements are often not easily translated to the rate equation approach most
commonly used in astrochemical modelling. Reducing the degree of uncertainty
intrinsic in these databases is therefore a prime problem, but has so far been
approached mainly by ad hoc procedures of essentially trial and error. In this
chapter we review the problem of the determination of accurate and complete
chemical networks in the wider context of Astrochemistry and explore the
possibility of using statistical methods and machine learning (ML) techniques
to reduce the uncertainty in chemical networks.
