Computational Screening for Developing Optimal Intermetallic Transition Metal Pt-Based ORR Catalysts at the Predictive Volcano Peak

Practical development of cost-effective and environmentally sustainable oxygen reduction reaction (ORR) catalysts has not yet been fully realized despite years of effort. Specifically, state-of-the-art ORR catalysts typically require high Pt-loading while still suffering significant overpotential losses and only providing moderate current density. In this work, we present results from new DFT calculations screening a wide range of Pt3(MN)1 ternary catalysts to see the range of values for oxygen adsorption energy (the known ORR volcano plot descriptor) such systems can achieve. Our results identify many promising materials based purely on performance via this descriptor; unfortunately, many of these promising candidates still require a very high loading of rare precious transition metals in the guest (MN) composition role. We have further used our results to generate a predictive fitted model for the ORR descriptor value itself based on the normalized valence and mass of a given fcc(111) Pt3(MN)1 surface compared to Pt(111). This predictive model is presented to help illustrate and guide the selection of further candidate systems in the continued search for the rational design of cost-effective, high-performance Pt-based ORR catalysts.