Data-driven algorithm yields three unique ZIFs with high selectivity for greenhouse gas separation
A collaborative research effort between UNIST and the Korea Institute of Science and Technology (KIST) has led to the successful synthesis of three novel porous materials by leveraging a data-driven structure prediction algorithm. These newly developed materials, modeled after zeolites, represent metal-organic frameworks (MOFs) with exceptional selectivity in gas separation, particularly for carbon dioxide (CO2).