Quantitative Structure–Property Relationship Models for Recognizing Metal Organic Frameworks (MOFs) with High CO2 Working Capacity and CO2/CH4 Selectivity for Methane Purification

Aghaji, Mohammad Zein; Fernández, Michael; Boyd, Peter G.; Daff, Thomas D.; Woo, Tom K.

doi:10.1002/ejic.201600365

Cited by 89 publications

(73 citation statements)

References 32 publications

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“…of Ottawa database contains the largest number of MOFs that exceed IRMOF-20 in terms of UV and UG capacities; 3581 MOFs surpass this threshold. Similarly, 2154 and 222 MOFs from the Northwestern and remaining (combined) hypothetical MOF databases, respectively, show promise for achieving capacities in excess of IRMOF-20 32,44 . Supplementary Tables 4–10 list the 20 highest capacity MOFs from each database in Table 2 that exceed the volumetric capacity of IRMOF-20.…”

Section: Resultsmentioning

confidence: 99%

“…with zero surface areaCapacity exceeds MOF-5Exceeds IRMOF-20Real MOFs: 8,24,25 UM+CoRE+CSD15,2352950405102Mail-order 35 11243019In silico deliverable 34 2816154276In silico surface 56 8, 88528323677MOF-74 analogs 58 61000ToBaCCo 17 13,51221413572Zr-MOFs 27 204012635Northwestern 32 137,00030,16043972154Univ. of Ottawa 44 315,61532,99376123581In-house1801813Total493,45866,75812,9866059Details of the MOF database, including the number of MOFs in a given database, the number with negligible internal surface area, and the number of compounds identified by GCMC that exceed the usable, pressure-swing capacities of MOF-5 and IRMOF-20. Additional details can be found in Methods Section, Supplementary Methods, Supplementary Figure 1 and Supplementary Tables 1-10…”

Section: Resultsmentioning

confidence: 99%

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Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

et al. 2019

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Few hydrogen adsorbents balance high usable volumetric and gravimetric capacities. Although metal-organic frameworks (MOFs) have recently demonstrated progress in closing this gap, the large number of MOFs has hindered the identification of optimal materials. Here, a systematic assessment of published databases of real and hypothetical MOFs is presented. Nearly 500,000 compounds were screened computationally, and the most promising were assessed experimentally. Three MOFs with capacities surpassing that of IRMOF-20, the record-holder for balanced hydrogen capacity, are demonstrated: SNU-70, UMCM-9, and PCN-610/NU-100. Analysis of trends reveals the existence of a volumetric ceiling at ∼40 g H 2 L −1 . Surpassing this ceiling is proposed as a new capacity target for hydrogen adsorbents. Counter to earlier studies of total hydrogen uptake in MOFs, usable capacities in the highest-capacity materials are negatively correlated with density and volumetric surface area. Instead, capacity is maximized by increasing gravimetric surface area and porosity. This suggests that property/performance trends for total capacities may not translate to usable capacities.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

et al. 2019

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“…In standard molecular mechanics type force fields, there are bonding terms and non-bonding terms; the latter are typically partitioned into the electrostatic terms and van der Waals terms. It is common practice to simulate MOFs as rigid, [314,315,319] taking the van der Waals parameters from generic force fields, such as the Universal [316] and Dreiding [320] force field. The issue of assigning partial charges is more complex and is discussed in Section 5.3.…”

Section: Methodsmentioning

confidence: 99%

“…[348] Whilst not a limitation-free metric, analytical predictions such as these are effective descriptors for reducing the search space needed for the identification of high performance candidates. The methods of database construction mentioned in the studies above are based on known building units extrapolated from crystallographic data; [314,315] these techniques rely on exploration of MOF space using libraries of pre-existing linker species. This is a limitation of the existing databases as sampling of composition space is restricted by the input of pre-defined building units (often commodity chemicals).…”

Section: Methane Storagementioning

confidence: 99%

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

et al. 2020

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Notwithstanding these impediments, in many fields of materials science, solutions are being designed to mitigate hindrances to the efficient sampling of chemical space and improving the robustness of computational screening models through a combination of high throughput synthesis (HTS), characterization, and machine learning. In this review, we highlight key developments in high throughput approaches pertinent to porous materials. Specifically, we focus on developments in the field of zeolitic materials, metal-organic frameworks (MOFs), and touch upon covalent organic frameworks (COFs). Although superficially these classes of materials have little in common, there are structural links such as topology which allow for the consideration of how high throughput methods contrast with one another. By contrasting developments in different fields, we identify some underutilized approaches which could be leveraged in the future. We target structurally ordered materials because the exploration of chemical and topological space is more straightforward to enumerate, though similar approaches could be applied to disordered materials. The scale of the problem faced in materials science of targeted structure and function [1] is by no means unique; in drug discovery, HTS and chemoinformatic approaches have been used for more than 40 years in an attempt to accelerate the discovery of druggable molecules. [2,3] Unlike the drug discovery process, where Lipinski's "rule of 5" [4] provides a reliable top level sift for viable targets, identifying the most promising material for a target application requires very particular properties that may be intertwined in complex and contradictory ways. For example, thermoelectrics require high electrical conductivity and low thermal conductivity and yet these properties are typically correlated unless they can be separated. [5] Given the large scope of potential applications, the advent of the Materials Genome Initiative (MGI) [6,7] in the United States has undoubtedly helped to promote ways to solve the aforementioned obstacles and numerous others. Similarly there are major initiatives within the EU (e.g., NOMAD [8] and BIGmax [9]) and Switzerland (MARVEL [10]). In the MGI, nanoporous materials, including zeolites and MOFs, have been specifically targeted [11] and in this review, we seek to highlight particular challenges in the field of porous materials and how researchers have sought to overcome these challenges. We focus primarily on the methods used in HTS and rapid throughput/screening computational approaches. Aspects of high throughput computation applied to porous materials have been reviewed before, [12-14] as has high throughput experimentation (HTE) [15,16] but here we focus on their combination within an integrated workflow. Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre-and post-synthetic computer simulations are mostly carried out in a piecemeal a...

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“…if we are given a structure, can we predict its performance, say in CO 2 separations, based on its surface area, pore size, or pore volume? There have been several recent studies dedicated to answering this question using models developed by machine learning 32,45,114,[131][132][133][134] , a field that is becoming extremely powerful in materials science 135,136 . It was shown that 1-dimensional geometric descriptors are able to successfully predict adsorption at high pressures 134,137 and low temperatures 114 , using representative datasets of materials to train machine learning models.…”

Section: H1 Data Mining Approachesmentioning

confidence: 99%

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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Quantitative Structure–Property Relationship Models for Recognizing Metal Organic Frameworks (MOFs) with High CO₂ Working Capacity and CO₂/CH₄ Selectivity for Methane Purification

Cited by 89 publications

References 32 publications

Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Computational development of the nanoporous materials genome

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