Uncovering Zeolitic Imidazolate Frameworks through Digitization of Chemical Insights

The vast number of computational predictions presents challenges when transitioning from structural models to experimental confirmations. To address this challenge, we digitized chemical intuition into the discovery process, focusing on zeolitic imidazolate frameworks (ZIFs). Despite their potential, the limited topologies by the “zeolite conundrum” and an unclear synthetic roadmap have hindered ZIF discovery. We propose a data-driven approach for ZIF discovery using structural descriptors of known materials as a screening tool. From over 4 million zeolite structures, we identified potential ZIF candidates based on O−T−O angle differences, vertex symbols, and T−O−T angles. Energy calculations enabled the ranking of synthesizability of ZIFs, resulting in the successful synthesis of three ZIFs with two unprecedented topologies, UZIF-31 (uft1) and UZIF-32, -33 (uft2). Notably, UZIF-33 demonstrated remarkable selective adsorption of CO2. This work underscores the synergistic potential of combining structure predictions with chemical intuition to advance the field of material discovery.