Viti, S; Holdship, J; (2020) Constraining Chemical Networks in Astrochemistry. In: Cartwright, HM, (ed.) Machine Learning in Chemistry: The Impact of Artificial Intelligence. (pp. 195-205). Royal Society of Chemistry: London, UK.

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Abstract

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 and experimentally studied. Moreover, experimental measurements are often not easily translated to the rate equation approach that is most commonly used in astrochemical modelling. Reducing the degree of uncertainty intrinsic in these databases is, therefore, a prime problem, but it 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.

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