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

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 Q(2) = 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 R(2) = 0.95 and Q(2) = 0.86, respectively.