Data-Driven Discovery of New Zeolitic Imidazolate Frameworks

Abstract

The discovery of new advanced materials is important for sustainable future. Traditionally, the materials discovery rely heavily on synthetic scientists through repeated trial-and-error experiments. Theorists commonly utilized the large-sized materials database to seek the optimal materials for desired properties. Such computational approach has been quite successful in revealing the performance limits of porous materials, but often extremely difficult to predict which materials could be synthesizable from the millions of candidates. We hypothesize that collective structural signatures, extracted from known porous solids, could be served as structural descriptors, which then could be efficient data filters for screening porous materials. In this presentation, we show not-yet-synthesized ZIF topologies, filtered and modelled from both real and hypothetical zeolite databases. To filer the entries in these databases, we have identified three structural descriptors. Together with these new candidate topologies for ZIFs, we also suggest possible synthetic strategies to realize these future ZIFs.