Surface Nanostructures of Pt-Compositionally Complex Alloy Single-Crystal Model Catalyst Surfaces for Improved Oxygen Reduction Reaction: Machine-Learning-Assisted Exploration

We investigated oxygen reduction reaction (ORR) properties of Pt-containing compositionally complex alloy (Pt-CCA) single-crystal model catalyst surfaces to optimize dry-process synthesis conditions, that is, CCA compositions of less-noble alloying elements and their synthesis (annealing) temperatures. Using a machine-learning approach, we effectively navigated the large space of possible synthesis conditions to minimize the experimental workload. The ORR activity and durability of the Pt/CCA/Pt(111) model catalyst surfaces (synthesized through vacuum deposition on a Pt(111) substrate of nonequiatomic Cr–Mn–Fe–Co–Ni or Mn–Fe–Co–Ni alloy (111) lattice stacking layers, followed by a surface Pt(111) layer) depend upon the alloy composition and synthesis temperature: the model catalyst surfaces synthesized with specific combinations of these two parameters outperformed benchmark surfaces such as Pt/equiatomic Cr–Mn–Fe–Co–Ni/Pt(111) in terms of the ORR durability during potential-cycle loading. The outstanding ORR properties are attributed to the use of machine learning to predict synthesis conditions that are closely linked to the atomic-level surface microstructures that favor enhanced ORR properties. These microstructures enable the formation of a so-called “pseudo-core-shell-like structure”, i.e., surface Pt(111) underlaid with CCA(111) lattice stacking layers with atomically distributed active elements (Co and/or Ni) close to the surface that are beneficial for ORR property enhancements. This study demonstrates that not only the “high-entropy” effect of charged less-noble CCA elements but also the precise control of elemental distributions in the near-surface vicinity in the pristine state, resulting from optimized CCA compositions and synthesis temperatures, are the key factors to improve Pt-CCA catalyst material systems.