In the quest for clean energy, discovery of new catalysts is a pressing issue. There are several reasons for that, among which, first, presently the most efficient and stable catalysts for the chemical processes that we use to transform raw resources into products with the desired functions, contain expensive and rare elements such as Pt, Ir, and Ru. This explains the efforts to find abundant, low-cost, stable alternatives that will yield process efficiencies comparable to those we have today. For example, for the oxygen evolution reaction (OER), many new materials with different compositions have shown promising results as catalysts. However, they are mostly prepared by wet chemical synthesis, which results in chemical waste and can be too slow for industrial use. Second, the morphology of the materials is important, because it affects their catalytic properties as higher surface areas yield more catalytic active sites, surface energetics change, leading to improved reaction rates, and other effects on catalytic activity. These reasons emphasize the motivation to accelerate the process of finding new materials with varying nanostructures and optimized functionality, by systematic exploration of parameter spaces.<br/>Here we present the progress in the development of catalysts using physical vapor deposition, which eliminates the commonly used wet chemical steps, to form different types of material compositions and nanostructures as functional electrocatalysts for OER. To this end, we have used combinatorial materials synthesis of material libraries prepared by sputtering and glancing angle deposition (GLAD). We then use in-house-built high-throughput scanning systems to investigate the materials libraries' chemical, physical, morphological, and electrocatalytic properties. For example, a scanning electrochemical system that can go to predetermined positions on the library to measure a specific composition. The insights we gain, show a dependence of catalytic activity on composition and nanostructuring, which the standard experimental techniques cannot explore, thus illustrating the importance and impact that composition and structure have on developing sustainable catalysts. As a result, these parameter spaces must be incorporated into the study of future electrocatalysts. This can be done by high-throughput experimentation design, combined with machine learning tools that will assist with appropriate path directions and ensure rational studies on catalysis.

High-throughput electrocatalyst design for the oxygen evolution reaction

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