  Surface adsorption is one of the fundamental processes in numerous fields,
including catalysis, environment, energy and medicine. The development of an
adsorption model which provides an effective prediction of binding energy in
minutes has been a long term goal in surface and interface science. The
solution has been elusive as identifying the intrinsic determinants of the
adsorption energy for various compositions, structures and environments is
non-trivial. We introduce a new and flexible model for predicting adsorption
energies to metal substrates. The model is based on easily computed, intrinsic
properties of the substrate and adsorbate. It is parameterised using machine
learning based on first-principles calculations of probe molecules (e.g.,
H$_2$O, CO$_2$, O$_2$, N$_2$) adsorbed to a range of pure metal substrates. The
model predicts the computed dissociative adsorption energy to metal surfaces
with a correlation coefficient of 0.93 and a mean absolute error of 0.77 eV for
the large database of molecular adsorption energies provided by
\textit{Catalysis-Hub.org} which have a range of 15 eV. As the model is based
on pre-computed quantities it provides near-instantaneous estimates of
adsorption energies and it is sufficiently accurate to eliminate around 90\% of
candidates in screening study of new adsorbates. The model, therefore,
significantly enhances current efforts to identify new molecular coatings in
many applied research fields.
