Data mining for predicting gas diffusivity in zeolitic-imidazolate frameworks (ZIFs)

Molecular sieving is based on mobility differences of species under extreme confinement, i.e. within pores of molecular dimensions. The pore properties of a material determine its separation efficiency, while pore network engineering provides a way to optimize the sieving performance. Unlike rigid and structurally limited carbon and zeolite molecular sieves, metal organic frameworks (MOFs) offer flexible networks with unlimited pore tailoring possibilities, by using different linkers, functional groups and metals/clusters. Nevertheless, knowledge-based pore optimization towards highly selective materials is hampered by the complex relationship between structural modifications and molecular diffusivity. Machine learning (ML) approaches can elucidate this correlation, but pertinent research in MOFs has so far focused solely on sorption properties. Herein, we report the first ML-assisted work towards understanding how the replacement of basic MOF building units affects the pore structure and consequently the molecular diffusivity. The ML approach developed is general; the work is however focused on zeolitic-imidazolate frameworks (ZIFs) with SOD topology. Since there is no database of relevant ZIF variations, we constructed a new ensemble of 72 existing and new ZIFs through systematic sub-unit replacement, developed a force-field for each of these structures and performed molecular dynamics (MD) simulations on fully flexible systems to calculate framework properties and the diffusivity of different molecules (ranging from helium to n-butane). Based on this new database, a predictive multi-step ML model was developed and trained. The model can rapidly and efficiently estimate the diffusivity of molecules in any possible ZIF structure with SOD topology by using readily accessible input information.