Prediction of O and OH Adsorption on Transition Metal Oxide Surfaces from Bulk Descriptors

In the search for stable and active catalysts, density functional theory and machine learning-based models can accelerate the screening of materials. While stability is conveniently addressed on the bulk level of computation, the modeling of catalytic activity requires expensive surface simulations. In this work, we develop models for the surface adsorption energy of O and OH intermediates across a consistent and extensive data set of pure transition metal oxide surfaces. We show that adsorption energies across metal oxidation states of +2 to +6 are well captured from the metal–oxygen bond strength extracted from the bulk level calculation. Specifically, we calculate the integrated crystal orbital Hamiltonian population (ICOHP) of the metal–oxygen bond in the bulk oxide and employ a simple normalization scheme to obtain a strong correlation with the adsorption energetics. By combining our ICOHP descriptor with non-DFT features in a Gaussian Process regression (GPR) model, we achieve a high model accuracy with mean absolute errors of 0.166 and 0.219 eV for OH and O adsorption, respectively. By targeting the adsorption energy difference of the OH–OH adsorption with our GPR model, we predict the oxygen evolution reaction activity from bulk descriptors only. Furthermore, we utilize the strong correlation between the COHP and metal–oxygen bond lengths to rapidly predict the adsorption energetics and catalytic activity from the optimized bulk geometry. Our approach can enable an efficient search for active catalysts by eliminating the need for surface calculations in the initial screening phase.