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Finding the "ideal" catalyst is a matter of great interest in the communitiesof chemists and material scientists, partly because of its wide spectrum ofindustrial applications. Information regarding a physical parameter termed"adsorption energy", which dictates the degrees of adhesion of an adsorbate ona substrate is a primary requirement in selecting the catalyst for catalyticreactions. Both experiments and \textit{in-silico} modelling are extensivelybeing used in estimating the adsorption energies, both of which are\textit{Edisonian} approach and demands plenty of resources and are timeconsuming. In this report, by employing a data centric approach almostinstantly we predict the adsorption energies of atomic and molecular gases onthe surfaces of many transition metals (TMs). With less than 10 sets of simpleatomic features, our predictions of the adsorption energies are within aroot-mean-squared-error (RMSE) of less than 0.4 eV with the quantum many-bodyperturbation theory estimates, a computationally expensive with goodexperimental agreement. Further, we minimized the RMSE up to 0.11 eV by usingthe precomputed adsorption energies obtained with conventional exchange andcorrelation (XC) functional as one component of the feature vector. Based onour results, we developed a set of scaling laws between the adsorption energiescomputed with many-body perturbation theory and conventional DFTXC-functionals.

Error Minimization in Predicting Accurate Adsorption Energies Using Machine Learning