From Data to Discovery: Crafting Data‐Driven Frameworks for Advancing OER Electrocatalysts

Electricity‐driven water splitting presents significant potential for storing electrical energy in the form of hydrogen gas. However, its overall efficiency is limited by the oxygen evolution reaction (OER), a kinetically sluggish reaction. Overcoming this limitation requires the development of high‐performance, cost‐effective, and durable catalysts. However, traditional screening methods are time‐ and resource‐intensive due to the vast chemical space, hindering the development of OER electrocatalysts. Recently, machine learning (ML) has emerged as a powerful tool for accelerating materials discovery by uncovering structure‐property relationships. This review provides a comprehensive analysis of strategies for developing ML models tailored to various research perspectives in the design of OER electrocatalysts. We begin by outlining the key components of ML models, followed by an in‐depth discussion of the strategies for assembling ML frameworks with specific objectives in OER catalyst design. Subsequently, we classify recent advances in ML applications to OER electrocatalysis into three main areas: using ML to investigate the formability of OER electrocatalysts, studying the activity of OER electrocatalysts, and assist the characterization of OER electrocatalysts. Finally, we discuss the challenges of integrating ML into OER research and highlight future opportunities to utilize ML to revolutionize the development of OER electrocatalysts.