Toward a new definition of surface energy for late transition metals

Surface energy is a top-importance stability descriptor of transition metal-based catalysts. Here, we combined DFT calculations and a tiling scheme measuring surface areas of metal structures to develop a simple computational model based on Lorentzian trends predicting the average surface energy of the metal structure independently of their shape. We also used machine-learning protocols to build a Multi-Layer Perceptron algorithm improving the Lorentzian trend’s accuracy and with the ability to predict the surface energies of metal surfaces, nanoparticles, and sub-nanometer clusters, with a Mean Absolute Error of 0.091 J/m² at minimal computation cost