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.