Error Minimization in Predicting Accurate Adsorption Energies Using Machine Learning

Finding the "ideal" catalyst is a matter of great interest in the communities of chemists and material scientists, partly because of its wide spectrum of industrial applications. Information regarding a physical parameter termed "adsorption energy", which dictates the degrees of adhesion of an adsorbate on a substrate is a primary requirement in selecting the catalyst for catalytic reactions. Both experiments and \textit{in-silico} modelling are extensively being used in estimating the adsorption energies, both of which are \textit{Edisonian} approach and demands plenty of resources and are time consuming. In this report, by employing a data centric approach almost instantly we predict the adsorption energies of atomic and molecular gases on the surfaces of many transition metals (TMs). With less than 10 sets of simple atomic features, our predictions of the adsorption energies are within a root-mean-squared-error (RMSE) of less than 0.4 eV with the quantum many-body perturbation theory estimates, a computationally expensive with good experimental agreement. Further, we minimized the RMSE up to 0.11 eV by using the precomputed adsorption energies obtained with conventional exchange and correlation (XC) functional as one component of the feature vector. Based on our results, we developed a set of scaling laws between the adsorption energies computed with many-body perturbation theory and conventional DFT XC-functionals.