Reducing energy requirements of gas separations by computational material design of metal-organic frameworks

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Gas separations are some of the most energy-intensive chemical processes worldwide. The environmental footprint of these separations would be immensely reduced by replacing the conventional cryogenic distillation with an adsorption-based process [1]. The main reason why this has not yet happened is the lack of adsorbent materials with the ideal characteristics to make each gas separation process viable. This project aims to deploy a synergistic effort between experiments and computational modelling to explore new nanoporous Metal-Organic Framework (MOF) materials for challenging gas separations. These are crystalline organometalic frameworks that allow for tunable design of both pore structure and surface chemistry to suit particular applications. Exciting developments have been recently reported on using MOFs that contain Coordinatively Unsaturated Sites (CUS) [2]. These sites arise on MOF materials after the removal of solvent molecules attached to the metal sites during synthesis. Material activation thus yields "vacant" sites that can bind strongly and specifically to electron-donating molecules through coordination-type bonds, dramatically increasing the material's selectivity. The wide variety of different MOF structures that exist (~70000) or can potentially be synthesised (>500000) makes it impossible to screen over such a large number of materials using expensive and time-consuming experiments. Computational tools like molecular simulation allow fast and cheap screening of MOFs for a particular application [3]. However, the main hurdle preventing high-throughput screening efforts to be turned into de facto computational material design is the lack of appropriate molecular models that can accurately describe the interactions between gas molecules and the CUS. Indeed, it has been shown that conventional models are unable to handle this kind of interaction, and that multi-scale approaches that combine quantum chemistry (QM) with classical Monte Carlo (MC) simulations are needed to address such complex problems [4]. The present project will build upon a hybrid QM/MC model developed in the Jorge group [5] and apply it to predict adsorption in a large number of MOFs with CUS under industrially relevant separation conditions [1]. Case studies will involve separations in the presence of water vapour (e.g. carbon capture), which are among the most challenging processes to design. The results of these simulations will be used to train a machine-learning algorithm, in collaboration with the group of Gómez-Gualdrón at Colorado [3], to explore the full set of existing and hypothetical MOFs. Once a small subset of MOFs with the most promising performance is identified, they will be experimentally synthesised, characterised and tested for adsorption separations in the Fletcher lab, thus closing the loop on the material design cycle. [1] Sholl, and Lively; Nature 2016, 532, 435-437.[2] Bachman et al.; J. Am. Chem. Soc. 2017, 139, 15363-15370.[3] Anderson et al.; Chem. Mater. 2018, 30, 6325-6337.[4] Fischer et al.; Mol. Simul., 2014, 40, 537-556.[5] Jorge, M.; Ind. Eng. Chem. Res., 2014, 53, 15475-15487.