Design rules for ternary CO₂ hydrogenation catalysts via literature-sourced network construction and analysis

The development of CO₂ hydrogenation catalysts has largely depended on researchers's trial and error efforts. However, exploring unreported ternary catalysts requires substantial time and cost, highlighting the need for more efficient screening strategies. This study adopts a data-driven informatics approach where literature data is restructured into networks to reveal systematic relationships between reaction conditions and CO₂ conversion. Knowledge extracted from the catalyst combination network further enables the identification of promising ternary catalysts. NiMnPr/Al₂O₃ and NiMnCe/Al₂O₃ for CO₂ hydrogenation are rapidly identified and experimentally validated, exhibiting higher CO₂ conversion than their corresponding binary catalysts. Moreover, detailed characterization is carried out for NiMnPr/Al₂O₃. These findings and analysis demonstrate that mapping multidimensional data into networks provides a powerful strategy for uncovering correlated variables and facilitating intuitive, highly efficient catalyst development and understanding.