Machine learning driven advancements in catalysis for predicting hydrogen evolution reaction activity

In the field of catalysis research, the emergence of machine learning (ML) has triggered a significant transformation, revolutionizing our methodologies for exploring and comprehending the dynamics of hydrogen evolution reaction (HER) activity. This review explores the burgeoning adoption of ML techniques in catalysis research, with a particular focus on their application in predicting HER activity. The review begins with an introduction to the ML workflow and its relevance in predicting catalytic performance. Emphasis is given to the significance of data quality and quantity, highlighting the need for well-defined input variables and the continuous evolution of catalysis-specific databases. We also accentuates the pivotal role of descriptors in utilizing ML for HER activity prediction, emphasizing the importance of proper selection based on database size and features to capture domain knowledge of diverse material properties. Furthermore, this review comprehensively examines the application of ML techniques in the context of HER, enabling accurate predictions of catalytic performance. Finally, the review explores immediate research needs and outlines future directions in this rapidly evolving field.