  The behaviour of molecules in space is to a large extent governed by where
they freeze out or sublimate. The molecular binding energy is thus an important
parameter for many astrochemical studies. This parameter is usually determined
with time-consuming experiments, computationally expensive quantum chemical
calculations, or the inexpensive, but inaccurate, linear addition method. In
this work we propose a new method based on machine learning for predicting
binding energies that is accurate, yet computationally inexpensive. A machine
learning model based on Gaussian Process Regression is created and trained on a
database of binding energies of molecules collected from laboratory experiments
presented in the literature. The molecules in the database are categorized by
their features, such as mono- or multilayer coverage, binding surface,
functional groups, valence electrons, and H-bond acceptors and donors. The
performance of the model is assessed with five-fold and leave-one-molecule-out
cross validation. Predictions are generally accurate, with differences between
predicted and literature binding energies values of less than $\pm$20\%. The
validated model is used to predict the binding energies of twenty one molecules
that have recently been detected in the interstellar medium, but for which
binding energy values are not known. A simplified model is used to visualize
where the snowlines of these molecules would be located in a protoplanetary
disk. This work demonstrates that machine learning can be employed to
accurately and rapidly predict binding energies of molecules. Machine learning
complements current laboratory experiments and quantum chemical computational
studies. The predicted binding energies will find use in the modelling of
astrochemical and planet-forming environments.
