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.