Realizing Cocktail Effects in Catalytic High-Entropy Metal-Organic Frameworks (HEMOFs) via Predictive Density of...

High-entropy materials, particularly high-entropy metal-organic frameworks (HEMOFs), represent a promising class of catalysts amenable to imparting superior activity via harnessing cocktail effects. However, optimal leveraging of these effects remains an outstanding challenge. Here, we introduce a promising, computationally driven roadmap for the rational selection of metal compositions in catalytic HEMOFs. Density functional theory (DFT) was first used to probe a key catalytic intermediate for CO₂ epoxidation in a series of compositionally related high-entropy polynuclear clusters. A direct correlation between composition and predicted catalytic activity trends was established, capitalizing on clear differences in a new valence edge peak in the band gap as function of active metal site. Following that, a series of HEMOFs with DFT-predetermined compositions were successfully synthesized. Remarkably, catalytic tests demonstrated the trends predicted by DFT, emphasizing the direct correlation between electronic structure and activity. The DFT-predicted optimal HEMOF composition was validated experimentally and shown to exhibit over 40% greater activity than the least active variant. The strategy described herein demonstrates the immense promise of harnessing cocktail effects in HEMOFs, wherein synergistic intermetallic effects impart superior catalytic activity. Finally, this work serves as a first step toward enabling machine learning-driven approaches to optimize catalytic performance through tailored metal compositions.