Machine Learning‐Driven Innovations in Energy and Environmental Catalysis: A Minireview

ABSTRACT The integration of machine learning (ML) with density functional theory (DFT) is transforming the landscape of catalysis research, offering new avenues for the design and optimization of catalytic materials. This review explores the application of ML techniques in both nanoscale and macroscale catalyst design, highlighting how these methods predict catalyst performance, optimize reaction conditions, and accelerate high‐throughput screening. This review discusses the role of DFT in providing detailed insights into electronic structures and reaction mechanisms, which, when combined with ML, enhance the efficiency and precision of catalyst development. Case studies, including methanation reactions and selective catalytic reduction, illustrate the successful implementation of ML‐driven models in real‐world applications. Furthermore, the challenges and limitations of this combined approach were addressed, such as data quality, descriptor selection, model interpretability, and experimental validation. This review underscores the potential of ML and DFT to advance energy and environmental catalysis, while also identifying key areas for future research.