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Metal-organic frameworks (MOFs) are porous materials constructed from modular molecular building blocks, typically metal clusters and organic linkers. These can, in principle, be assembled to form an almost unlimited number of MOFs, yet materials reported to date represent only a tiny fraction of the possible combinations. Here, we demonstrate a computational approach to generate all conceivable MOFs from a given chemical library of building blocks (based on the structures of known MOFs) and rapidly screen them to find the best candidates for a specific application. From a library of 102 building blocks we generated 137,953 hypothetical MOFs and for each one calculated the pore-size distribution, surface area and methane-storage capacity. We identified over 300 MOFs with a predicted methane-storage capacity better than that of any known material, and this approach also revealed structure-property relationships. Methyl-functionalized MOFs were frequently top performers, so we selected one such promising MOF and experimentally confirmed its predicted capacity. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks-a class of porous hybrid materials built from metal ions and organic bridges-have recently shown great promise for a wide variety of applications. The large choice of building blocks means that the structures and pore characteristics of the metal-organic frameworks can be tuned relatively easily. However, despite much research, it remains challenging to prepare frameworks specifically tailored for particular applications. Here, we have used computational modelling to design and predictively characterize a metal-organic framework (NU-100) with a particularly high surface area. Subsequent experimental synthesis yielded a material, matching the calculated structure, with a high BET surface area (6,143 m2 g−1). Furthermore, sorption measurements revealed that the material had high storage capacities for hydrogen (164 mg g−1) and carbon dioxide (2,315 mg g−1)-gases of high importance in the contexts of clean energy and climate alteration, respectively-in excellent agreement with predictions from modelling. [ABSTRACT FROM AUTHOR]