Dynamic Phase Evolution of Fe‐Based Catalysts During CO ₂ Hydrogenation: Implications for Data–Driven Design

ABSTRACT

The thermocatalytic hydrogenation of carbon dioxide into value‐added hydrocarbons represents a crucial strategy for sustainable carbon utilization. Iron (Fe)‐based catalysts are key candidates for this process. However, they undergo significant dynamic structural transformations, evolving into complex multi‐phase ensembles of oxides and carbides under reaction conditions. This review systematically examines the dynamic phase evolution of Fe‐based catalysts, elucidating how pretreatment protocols, promoters, and support architectures influence active site transformations. State‐of‐the‐art operando and in situ characterization techniques are highlighted for their ability to monitor real‐time structural and kinetic changes. A critical analysis of current data‐driven methodologies reveals a bottleneck in the reliance on static descriptors. Consequently, a paradigm shift toward incorporating dynamic function descriptors derived from in situ‐defined structural and kinetic variables into machine learning frameworks is proposed. By bridging the gap between the dynamic physical reality of working catalysts and predictive modeling, this review offers transformative insights for the rational and advanced design of optimized catalytic systems for CO ₂ valorization, moving beyond trial‐and‐error approaches toward a more robust, data‐driven development strategy.