Machine-Learning-Guided Design of Pd–Au Electrocatalysts for the Oxygen Reduction Reaction

Abstract

Machine learning has significantly reduced the computational power necessary to estimate the free energy of adsorption of key reaction intermediates on a diverse range of catalytic surfaces. Nevertheless, translating this computational capability into the discovery and experimental validation of catalysts necessitates targeting specific questions where this faster computation can yield the most significant impact. Using the electrochemical oxygen reduction reaction (ORR) as a model reaction due to its known linear scaling relationships between key catalytic intermediates, we demonstrate that the Open Catalyst Project’s machine-learning-based calculations of adsorption energies can inform experimental catalytic research. The primary challenge we addressed was the structural effect of the ORR, wherein the higher Miller index facets of Pt exhibit diminished ORR activity in comparison to Pt(111). The Open Catalyst Project’s rapid relaxation energy calculations enabled us to screen a large number of bimetallic materials for each key intermediate of the ORR over a wide range of crystal facets. The Open Catalyst Project was able to identify that PdAu3 can overcome the negative structural effects observed on the higher Miller index facets of Pt for the ORR. Electrochemical experimentation via rotating disk electrode linear sweep voltammetry and Tafel slope analysis revealed that polycrystalline PdAu3 nanoparticles exhibit an improved onset potential for the ORR compared to commercial polycrystalline Pt/C. Thus, this study demonstrates an example of how machine-learning-driven materials discovery can accelerate catalyst design while simultaneously offering an experimental solution to the persistent challenge of high ORR overpotential.