Predicting adsorption on metals: simple yet effective descriptors for surface catalysis

Abstract

We present a simple and efficient model for predicting the adsorption of molecules on metal surfaces. This heuristic model uses six descriptors for each metal (number of d-electrons, surface energy, first ionization potential and atomic radius, volume and mass) and three for each adsorptive (HOMO–LUMO energy gap, molecular volume and mass). Strikingly, despite its simplicity and low computational cost, this model predicts well the chemisorption of a range of adsorptives (H₂, HO˙, N₂, CO, NO, O₂, H₂O, CO₂, NH₃ and CH₄) on a range of metals (Fe, Co, Ni, Cu, Mo, Ru, Rh, Pd, Ag, W, Ir, Pt and Au) as calculated with DFT and taken from the literature. Using only a third of the data for fitting, the rest of the data were predicted with Q² = 0.91–0.95 and RMSEP = 0.94–1.16 eV. Furthermore, we measured experimental adsorption data for CO, CO₂, CH₄, H₂, N₂ and O₂ on Ni, Pt and Rh supported on TiO₂. Using the same descriptors, we then constructed a model for this experimental data set. Once again, the model explained the data well, with R² = 0.95 and Q² = 0.86, respectively.