MACE: a Machine-learning Approach to Chemistry Emulation

The Journal of Open Source Software, vol. 10, pp. 7148

Abstract

Astrochemistry is the study of chemical species in astrophysical objects.By combining observations and theoretical predictions, the physical conditions of the object can be analysed as well as its chemical composition and yields, since chemistry is closely coupled to the dynamics of the environment.Most often, such astrophysical environments are known to be complex and out of thermodynamic equilibrium.For that reason, the chemical evolution is usually modelled using a chemical kinetics approach, in which a set of non-linear coupled ordinary differential equations (ODEs) is solved for a given network of chemical species and reactions.For large chemical networks, however, this method is computationally slow.Therefore, we developed MACE, a Machine-learning Approach to Chemistry Emulation, inspired by similar work in the literature, e.g., Holdship et al. (2021);Grassi et al. (2022);Sulzer & Buck (2023).Their code is publicly and can be found in their respective papers.However, not all of their code can be easily adapted or used as such.

Authors 4

1. Silke Maes

   KU Leuven

   Affiliation as printed

   Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium

2. Frederik De Ceuster

   KU Leuven

   Affiliation as printed

   Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium

   Leuven Gravity Institute, KU Leuven, Celestijnenlaan 200D, Leuven, Belgium

3. Marie Van de Sande Aachen Leiden Observatory

   Leiden University · University of Leeds · Leiden Observatory

   Affiliation as printed

   Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Netherlands

   School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, United Kingdom

4. L. Decin

   University of Leeds · KU Leuven

   Affiliation as printed

   Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Leuven, Belgium

   School of Chemistry, University of Leeds, Leeds LS2 9JT, United Kingdom