Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks

Zeolitic imidazolate frameworks (ZIFs), metal-organic analogues of zeolites, hold great potential for carbon-neutral applications due to their exceptional stability and porosity. However, ZIF discovery has been hindered by the limited topologies resulting from a mismatch between numerous predicted and few synthesized zeolitic networks. To address this, we propose a data-driven search algorithm 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 facilitated the ranking of ZIFs by their synthesizability, leading to the successful synthesis of three ZIFs with two novel topologies: UZIF-31 (uft1) and UZIF-32, -33 (uft2). Notably, UZIF-33 exhibited remarkable CO2 selective adsorption. This study highlights the synergistic potential of combining structural predictions with chemical intuition to advance material discovery.