Catalysis in the digital age: Unlocking the power of data with machine learning

The design and discovery of new and improved catalysts are driving forces for accelerating scientific and technological innovations in the fields of energy conversion, environmental remediation, and chemical industry. Recently, the use of machine learning (ML) in combination with experimental and/or theoretical data has emerged as a powerful tool for identifying optimal catalysts for various applications. This review focuses on how ML algorithms can be used in computational catalysis and materials science to gain a deeper understanding of the relationships between materials properties and their stability, activity, and selectivity. The development of scientific data repositories, data mining techniques, and ML tools that can navigate structural optimization problems are highlighted, leading to the discovery of highly efficient catalysts for a sustainable future. Several data‐driven ML models commonly used in catalysis research and their diverse applications in reaction prediction are discussed. The key challenges and limitations of using ML in catalysis research are presented, which arise from the catalyst's intrinsic complex nature. Finally, we conclude by summarizing the potential future directions in the area of ML‐guided catalyst development. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Data Science > Artificial Intelligence/Machine Learning Electronic Structure Theory > Density Functional Theory The present review article provides a practical guidance on using machine learning (ML) techniques for catalyst development, including data acquisition, feature engineering, model training, and validation. It explores combined ML and computational catalysis research to elucidate key materials properties essential for catalyst activity, selectivity, and stability.