Machine learning application in thermal CO2 hydrogenation: catalyst design, process optimization, and mechanism insights
Rasoul Salami, Tianlong Liu, Xue Han, Ying Zheng
Abstract
The growing demand for carbon neutrality has heightened the focus on CO 2 hydrogenation as a viable strategy for transforming carbon dioxide into valuable chemicals and fuels. Advanced machine learning (ML) approaches integrate materials science with artificial intelligence, enabling scientists to identify hidden patterns in datasets, make informed decisions, and reduce the need for labor-intensive, repetitive experimentation. This review provides a comprehensive overview of ML applications in the thermocatalytic hydrogenation of CO 2 . Following an introduction to ML tools and workflows, various ML algorithms employed in CO 2 hydrogenation are systematically categorized and reviewed. Next, the application of ML in catalyst discovery is discussed, highlighting its role in identifying optimal compositions and structures. Then, ML-driven strategies for process optimization, particularly in enhancing CO 2 conversion and product selectivity, are examined. Studies modeling descriptors, spanning catalyst properties and reaction conditions, to predict catalytic performance are analyzed. Consequently, ML-based mechanistic studies are reviewed to elucidate reaction pathways, identify key intermediates, and optimize catalyst performance. Finally, key challenges and future perspectives in leveraging ML for advancing CO 2 hydrogenation research are presented.