Panagiotis Krokidas, Stelios Karozis, Salvador Moncho, George Giannakopoulos, Edward N Brothers, Michael E Kainourgiakis, Ioannis G Economou, Theodore A Steriotis
Abstract:
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.