Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes

Abstract

Machine learning (ML) and artificial intelligence (AI) have been recognized as transformative tools in chemical engineering, offering opportunities for accelerated discovery, design, and optimization. In this work, the field of catalysis is selected as a case study to demonstrate how AI/ML can be employed to complement traditional experimental and computational approaches. The integration of ML across the full scope of catalytic reaction systems is investigated from initial catalyst screening and material design to reactor development, process monitoring, and real-time control. Co-occurrence analysis for catalysis and ML was performed using 12,743 author keywords from 3924 papers, while smaller subsets for author keywords were used to analyze the co-occurrence of ML for specific aspects of catalysis; the number of papers used to review and analyze the basic trends and findings was 174, as given in references. Applications of ML in analyzing catalytic performance, characterizing structures through spectroscopic data, developing kinetic and mechanistic models, and addressing transport limitations are highlighted. Emerging strategies such as physics-informed ML, hybrid frameworks, and generative AI are regarded as particularly promising for overcoming data scarcity, interpretability, and scalability challenges. A comprehensive view of the role of ML in catalysis is presented by tracing the evolution of the field and identifying future opportunities. Consequently, a roadmap is suggested for applying similar approaches to other complex chemical engineering processes.