Regulation of the Degree of Interpenetration in Metal–Organic Frameworks

Verma, Gaurav; Butikofer, Sydney; Kumar, Sanjay; Ma, Shengqian

doi:10.1007/s41061-019-0268-x

Cited by 36 publications

(30 citation statements)

References 134 publications

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“…The quest for extended MOFs with larger pores by linker elongation tends to weaken the robustness of frameworks. In this case, framework interpenetration, which usually leads to a smaller cavity and denser framework packing through beneficial framework–framework interactions, is able to enhance the thermodynamic stability and thus is preferred in products. − In typical MOF synthesis, particularly stable MOFs, a competing reagent or acid is often involved to inhibit the ligand substitution process during the MOF formation . As a result of the slow nucleation rate, highly crystalline products can be obtained.…”

Section: Resultsmentioning

confidence: 99%

Kinetically Controlled Reticular Assembly of a Chemically Stable Mesoporous Ni(II)-Pyrazolate Metal–Organic Framework

et al. 2020

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The application scope of metal–organic frameworks (MOFs) is severely restricted by their weak chemical stability and limited pore size. A robust MOF with large mesopores is highly desired, yet poses a great synthetic challenge. Herein, two chemically stable Ni(II)-pyrazolate MOFs, BUT-32 and -33, were constructed from a conformation-matched elongated pyrazolate ligand through the isoreticular expansion. The two MOFs share the same sodalite-type net, but have different pore sizes due to the network interpenetration in BUT-32. Controlled syntheses of the two MOFs have been achieved through precisely tuning reaction conditions, where the microporous BUT-32 was demonstrated to be a thermodynamically stable product while the mesoporous BUT-33 is kinetically favored. To date, BUT-32 represents the first example of Ni4-pyrazolate MOF whose structure was unambiguously determined by single-crystal X-ray diffraction. Interestingly, the kinetic product BUT-33 integrates 2.6 nm large mesopores with accessible Ni(II) active sites and remarkable chemical stability even in 4 M NaOH aqueous solution and 1 M Grignard reagent. This MOF thus demonstrated an excellent catalytic performance in carbon–carbon coupling reactions, superior to other Ni(II)-MOFs including BUT-32. These findings highlight the importance of kinetic control in the reticular synthesis of mesoporous MOFs, as well as their superiority in heterogeneous catalysis.

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Section: Resultsmentioning

confidence: 99%

Kinetically Controlled Reticular Assembly of a Chemically Stable Mesoporous Ni(II)-Pyrazolate Metal–Organic Framework

et al. 2020

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“…Generally, porous materials minimize the energy of the framework through optimal filling of void space, and thus, in cases where void space is of sufficient size, interpenetration may be observed. Controlling the degree of interpenetration has been explored using subtle changes in the synthetic methodology, such as varying reaction conditions, templating agents, and ligand design. , Given the increasing number of MOF crystal structures reported, the question arises as to whether we can postrationalize the extent to which the local geometry influences the tendency to interpenetrate.…”

Section: Resultsmentioning

confidence: 99%

Visualization and Quantification of Geometric Diversity in Metal–Organic Frameworks

et al. 2021

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With ever-growing numbers of metal−organic framework (MOF) materials being reported, new computational approaches are required for a quantitative understanding of structure−property correlations in MOFs. Here, we show how structural coarse-graining and embedding ("unsupervised learning") schemes can together give new insights into the geometric diversity of MOF structures. Based on a curated data set of 1262 reported experimental structures, we automatically generate coarse-grained and rescaled representations which we couple to a kernel-based similarity metric and to widely used embedding schemes. This approach allows us to visualize the breadth of geometric diversity within individual topologies and to quantify the distributions of local and global similarities across the structural space of MOFs. The methodology is implemented in an openly available Python package and is expected to be useful in future high-throughput studies.

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“…The field of QSAR is vast, and we refer readers to the following list of reviews for key historical technical developments. [57][58][59][60] For the purpose of this review, we will limit the scope of discussion to the performance of DNNbased QSAR models and appropriate comparisons to traditional machine learning models.…”

Section: Computer-aided Drug Designmentioning

confidence: 99%

Deep learning for computational chemistry

2017

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The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure prediction, quantum chemistry, materials design and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against nonneural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. 3 IntroductionDeep Learning is the key algorithm used in the development of AlphaGo, a Deep learning is a machine learning algorithm, not unlike those already in use in various applications in computational chemistry, from computer-aided drug design to materials property prediction. 5-8 Amongst some of its more high profile achievements include the Merck activity prediction challenge in 2012, where a deep neural network not only won the competition and outperformed Merck's internal baseline model, but did so without having a single chemist or biologist in their team. In a repeated success by a different research team, deep learning models achieved top positions in the Tox21 toxicity prediction challenge issued by NIH in 2014. 9 The unusually stellar performance of deep learning models in both predicting activity and toxicity in these recent examples, originate from the unique characteristics that distinguishes deep learning from traditional machine learning algorithms.For those unfamiliar with the intricacies of machine learning algorithms, we will highlight some of the key differences between traditional (shallow) machine learning and deep learning.The simplest example of a machine learning algorithm would be the ubiquitous least-squares linear regression. In linear regression, the underlying nature of the model is known (linear in th...

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Regulation of the Degree of Interpenetration in Metal–Organic Frameworks

Cited by 36 publications

References 134 publications

Kinetically Controlled Reticular Assembly of a Chemically Stable Mesoporous Ni(II)-Pyrazolate Metal–Organic Framework

Kinetically Controlled Reticular Assembly of a Chemically Stable Mesoporous Ni(II)-Pyrazolate Metal–Organic Framework

Visualization and Quantification of Geometric Diversity in Metal–Organic Frameworks

Deep learning for computational chemistry

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