Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies, and the state of the art catalysts are Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition-metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O*- or OH*-covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low-index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. On the basis of findings from DFT calculations, we provide catalyst design strategies to improve the catalytic activity of Ir-based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.