In silico prediction of MOFs with high deliverable capacity or internal surface area

Bao, Yi Wang; Martin, Richard L.; Harańczyk, Maciej; Deem, Michael W.

doi:10.1039/c5cp00002e

Cited by 36 publications

(40 citation statements)

References 43 publications

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“…Likewise it was shown that the CoRE database of experimental structures contains more than 350 unique topologies (again pcu ranking first with 16% of the structures) 39 . It was recently shown that different topologies can yield different minimum and maximum values for surface area and void volume, and the geometries of these structures have an implicit effect on performance [81][82][83] , particularly when considering properties that are more sensitive to geometry and chemistry, such as CO 2 adsorption at low partial pressures. Thus, in general, it would be advantageous to increase the range of topologies included in a database of materials.…”

Section: Figure 3: Challenges Associated With Assembling New Mofs Witmentioning

confidence: 99%

“…One simply needs a single representative SBU to orient on all the nodes in a net, as the net inherently expresses the chirality in its nodes. It is worthy of note that this algorithm (or earlier versions of it) have been used to generate several hypothetical databases of porous materials with a record number of topologies 31,33,63,83,91 . While the sum of these structures consist of roughly 50 topologies, the RCSR contains a total of 2719 nets as of this writing, leaving a large amount of room for further structural diversity 70 .…”

Section: H1 Database Development and The Quest For Diversitymentioning

confidence: 99%

“…Methane storage capacity is by far the most studied sorption property in the field of computational high throughput screening of nanoporous materials [27][28][29][30][31][32][33][34][35][36][37]83 . There are several reasons for this, the first being that there is considerable interest in safely and efficiently storing methane for use as an alternative, clean-burning fuel in motor vehicles.…”

Section: H2 Methane Storagementioning

confidence: 99%

“…Separating CO 2 from gas mixtures has become a major topic for materials screening studies 44,53,59,68,83,91 .…”

Section: H2 Carbon Dioxide Sequestrationmentioning

confidence: 99%

“…In lieu of brute-force screening studies, several works have demonstrated that more advanced search techniques such as a genetic algorithm (GA) can be used to identify top performing materials 59,83,91 . In these algorithms, the focus is on refining the search for a good material by modifying the materials 'genes' so that it becomes superior in later generations.…”

Section: H2 Carbon Dioxide Sequestrationmentioning

confidence: 99%

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Computational development of the nanoporous materials genome

2017

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H1 Abstract.There is currently a large push towards big data and data mining in materials research to accelerate discovery. Zeolites, metal-organic frameworks (MOFs) and other related crystalline porous materials are not immune to this recent phenomenon, as evidenced by the proliferation of porous structure databases and computational gas adsorption screening studies over the past decade. The strive to identify the best materials for a variety of gas separation and storage applications has not only led to collections of thousands of synthesised structures, but the development of hypothetical material building algorithms.The materials databases assembled with these algorithms expand greatly on the range of complex pore structures that have been synthesised, with the rationale that we have discovered only a small fraction of realisable structures and expanding upon these will accelerate rational design. In this review, we highlight some of the methods developed to build these databases, and some of the important outcomes resulting from large-scale computational screening efforts. SummaryIn the field of nanoporous materials discovery, we are witnessing the emergence of big data analytics combined with traditional computational thermodynamics calculations. This review turns a critical eye on the current state of the art, with a focus on computational database generation and results from large-scale screening for gas separations. H1 Introduction.The discovery, in the late 19 th century, that zeolites could trap interesting and valuable particles in their pores opened up an entire field of research 1,2 . This seemingly simple phenomenon was an enormous boon to the oil and gas industry, seeing as how cheap, but effective porous materials could serve as a catalyst for hydrocarbon cracking. From their humble beginnings (being carved out of rock faces), zeolites, and their ability to selectively trap guests in their pores, have become integral in not only the oil and gas industry, but in detergents (as ion exchangers) and natural gas purification to name a few 1 .Zeolites have since enjoyed an almost exclusive dominance in porous materials research until the late 20 th century, when we began to observe the creation of more diverse materials in terms of chemistry, network connectivity, and physical properties.At present, there are a little over 200 known zeolite structures, which is only a small fraction of the total number of structures that have been predicted 3 . In addition, if we were also able to expand the chemical diversity of these materials beyond the conventional Si 4+ , O 2-, and Al 3+ ions found in zeolites, one could envision designing materials for virtually any gas separation application. Thus when the first articles arose in the 1990's characterising Metal-Organic Frameworks (MOFs) [4][5][6][7][8] , and later Covalent Organic Frameworks (COFs) 9,10 , Zeolitic Imidazolate Frameworks (ZIFs) 11 , and Porous Polymer Networks (PPNs) 12 the excitement was palpable. It was recognised early-on by Yaghi, O'Keeffe, ...

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