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Surface adsorption is one of the fundamental processes in numerous fields,including catalysis, environment, energy and medicine. The development of anadsorption model which provides an effective prediction of binding energy inminutes has been a long term goal in surface and interface science. Thesolution has been elusive as identifying the intrinsic determinants of theadsorption energy for various compositions, structures and environments isnon-trivial. We introduce a new and flexible model for predicting adsorptionenergies to metal substrates. The model is based on easily computed, intrinsicproperties of the substrate and adsorbate. It is parameterised using machinelearning 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. Themodel predicts the computed dissociative adsorption energy to metal surfaceswith a correlation coefficient of 0.93 and a mean absolute error of 0.77 eV forthe large database of molecular adsorption energies provided by\textit{Catalysis-Hub.org} which have a range of 15 eV. As the model is basedon pre-computed quantities it provides near-instantaneous estimates ofadsorption energies and it is sufficiently accurate to eliminate around 90\% ofcandidates in screening study of new adsorbates. The model, therefore,significantly enhances current efforts to identify new molecular coatings inmany applied research fields.

A transferable prediction model of molecular adsorption on metals based on adsorbate and substrate properties