Issue |
A&A
Volume 666, October 2022
|
|
---|---|---|
Article Number | A45 | |
Number of page(s) | 26 | |
Section | Atomic, molecular, and nuclear data | |
DOI | https://doi.org/10.1051/0004-6361/202244091 | |
Published online | 04 October 2022 |
Predicting binding energies of astrochemically relevant molecules via machine learning
1
Department of Physics and Astronomy - Center for Interstellar Catalysis, Aarhus University,
8000
Aarhus C, Denmark
e-mail: mie@phys.au.dk
2
Physics Institute, University of Bern,
Sidlerstrasse 5,
3012
Bern, Switzerland
e-mail: niels.ligterink@unibe.ch
3
Aarhus Institute of Advanced Studies, Aarhus University,
8000
Aarhus C, Denmark
Received:
23
May
2022
Accepted:
29
June
2022
Context. The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is therefore an important parameter for many astrochemical studies. This parameter is usually determined with time-consuming experiments, computationally expensive quantum chemical calculations, or the inexpensive yet relatively inaccurate linear addition method.
Aims. In this work, we propose a new method for predicting binding energies (BEs) based on machine learning that is accurate, yet computationally inexpensive.
Methods. We created a machine-learning (ML) model based on Gaussian process regression (GPR) and trained it on a database of BEs of molecules collected from laboratory experiments presented in the literature. The molecules in the database are categorised by their features, such as mono- or multilayer coverage, binding surface, functional groups, valence electrons, and H-bond acceptors and donors.
Results. We assessed the performance of the model with five-fold and leave-one-molecule-out cross validation. Predictions are generally accurate, with differences between predicted binding energies and values from the literature of less than ±20%. We used the validated model to predict the binding energies of 21 molecules that were recently detected in the interstellar medium, but for which binding energy values are unknown. We used a simplified model to visualise where the snow lines of these molecules would be located in a protoplanetary disk.
Conclusions. This work demonstrates that ML can be employed to accurately and rapidly predict BEs of molecules. Machine learning complements current laboratory experiments and quantum chemical computational studies. The predicted BEs will find use in the modelling of astrochemical and planet-forming environments.
Key words: astrochemistry / ISM: molecules / molecular processes / molecular data
© T. Villadsen et al. 2022
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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