Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative structure–activity relationships to accelerate electrocatalyst design by learning from historic data without explicit programming. The algorithms, data/database, and descriptors are usually the decisive factors for ML and the descriptors play a pivotal role for electrocatalysis as they contain the essence of catalysis from the physicochemical nature. Despite the considerable research efforts regarding electrocatalyst design with ML, the lack of universal selection tactics for descriptors bridging the gap between structures and activity impedes its wider application. A timely summary of the application of ML in electrocatalyst design helps to deepen the understanding of the nature of descriptors and improve the application scope and design efficiency. This review summarizes the geometrical, electronic, and activity descriptors used as input for ML training and predicting to reveal the general rules for their application in the design of electrocatalysts. In response to the challenges of hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, CO 2 reduction reaction, and nitrogen reduction reaction, the ML application in these areas is tracked for the progress and prospective changes. Additionally, the potential application of the automated design and discovery are discussed for the other well‐known electrocatalytic processes.