(234a) Modeling Heterogeneous Electrocatalysis on Realistic Surfaces from First-Principles, Thermodynamics, and Machine Learning
Theoretical and computational approaches, most notably the d-band model for chemisorption scaling relations, have made important contributions to the understanding of heterogeneous electrocatalysis. Such approaches, however, implicitly assume that the catalytically-active surface for a particular class of materials is the same. We have discovered that the electrocatalytic activity of nickel phosphides (Ni2P, Ni5P4, and Ni3P) and calcium manganate (CaMnO3) toward the hydrogen and oxygen evolution reactions, respectively, are governed by aqueous surface equilibria, i.e. the most catalytically-active surfaces are aqueous surface reconstructions. Nickel phosphide surfaces react with phosphoric acid at small, reducing electrode potentials to form P-enriched surface reconstructions that offer nearly thermoneutral H adsorption. Conversely, for calcium manganate, it is Mn-depleted surface reconstructions, achieved via the formation of surface Mn vacancies, that activate lattice oxygen and lower the overpotential for oxygen evolution. In addition to the determination of realistic surfaces for computational studies of heterogeneous electrocatalysis, we are also developing combined first-principles and machine learning techniques for the automated discovery of catalytic descriptors. Using these techniques, we discovered that the Ni-Ni bond length is an excellent descriptor for the hydrogen evolving activity of Ni2P and that it can be modulated via nonmetal surface doping, which induces a chemical pressure effect.