Browsing by Author "Bao, Yi"

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    Computational discovery of metal-organic frameworks with high gas deliverable capacity
    (2017-04-20) Bao, Yi; Deem, Michael W.
    Metal-organic frameworks (MOFs) are a rapidly emerging class of nanoporous materials with largely tunable chemistry and diverse applications in gas storage, gas purification, catalysis, sensing and drug delivery. Efforts have been made to develop new MOFs with desirable properties both experimentally and computationally for decades. To guide experimental synthesis, we here develop a computational methodology to explore MOFs with high gas deliverable capacity. This de novo design procedure applies known chemical reactions, considers synthesizability and geometric requirements of organic linkers, and efficiently evolves a population of MOFs to optimize a desirable property. We identify 48 MOFs with higher methane deliverable capacity at 65–5.8 bar condition than the MOF-5 reference in nine networks. In a more comprehensive work, we predict two sets of MOFs with high methane deliverable capacity at a 65–5.8 bar loading–delivery condition or a 35–5.8 bar loading–delivery condition. We also optimize a set of MOFs with high methane accessible internal surface area to investigate the relationship between deliverable capacities and internal surface area. This methodology can be extended to MOFs with multiple types of linkers and multiple SBUs. Flexibile MOFs may allow for sophisticated heat management strategies and also provide higher gas deliverable capacity than rigid frameworks. We investigate flexible MOFs, such as MIL-53 families, and Fe(bdp) and Co(bdp) analogs, to understand the structural phase transition of frameworks and the resulting influence on heat of adsorption. Challenges of simulating a system with a flexible host structure and incoming guest molecules are discussed. Preliminary results from isotherm simulation using the hybrid MC/MD simulation scheme on MIL-53(Cr) are presented. Suggestions for proceeding to understand the free energy profile of flexible MOFs are provided.
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    In Silico Discovery of High Deliverable Capacity Metal-Organic Frameworks
    (American Chemical Society, 2015) Bao, Yi; Martin, Richard L.; Simon, Cory; Haranczyk, Maciej; Smit, Berend; Deem, Michael W.
    Metal-organic frameworks (MOFs) are actively being explored as potential adsorbed natural gas storage materials for small vehicles. Experimental exploration of potential materials is limited by the throughput of synthetic chemistry. We here describe a computational methodology to complement and guide these experimental efforts. The method uses known chemical transformations in silico to identify MOFs with high methane deliverable capacity. The procedure explicitly considers synthesizability with geometric requirements on organic linkers. We efficiently search the composition and conformation space of organic linkers for 9 MOF networks, finding 48 materials with higher predicted deliverable capacity (at 65 bar storage, 5.8 bar depletion, and 298 K) than MOF-5 in 4 of the 9 networks. The best material has a predicted deliverable capacity 8% higher than that of MOF-5.
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    In silico prediction of MOFs with high deliverable capacity or internal surface area
    (Royal Society of Chemistry, 2015) Bao, Yi; Martin, Richard L.; Haranczyk, Maciej; Deem, Michael W.
    Metal–organic frameworks (MOFs) offer unprecedented atom-scale design and structural tunability, largely due to the vast number of possible organic linkers which can be utilized in their assembly. Exploration of this space of linkers allows identification of ranges of achievable material properties as well as discovery of optimal materials for a given application. Experimental exploration of the linker space has to date been quite limited due to the cost and complexity of synthesis, while high-throughput computational studies have mainly explored MOF materials based on known or readily available linkers. Here an evolutionary algorithm for de novo design of organic linkers for metal–organic frameworks is used to predict MOFs with either high methane deliverable capacity or methane accessible surface area. Known chemical reactions are applied in silico to a population of linkers to discover these MOFs. Through this design strategy, MOF candidates are found in the ten symmetric networks acs, cds, dia, hxg, lvt, nbo, pcu, rhr, sod, and tbo. The correlation between deliverable capacities and surface area is network dependent.