Gold Catalysis in (Supra)Molecular Cages to Control Reactivity and Selectivity

Jans, Anne C. H.; Caumes, Xavier; Reek, Joost N. H.

doi:10.1002/cctc.201801399

Cited by 72 publications

(44 citation statements)

References 65 publications

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“…They have been added to a very recent review that encompasses earlier mentioned topics as well as the most recent approaches (e.g. using M 12 L 24 cages) …”

Section: Introductionmentioning

confidence: 99%

Au‐Cavitand Catalyzed Alkyne‐Acid Cyclizations

Schramm

2019

Eur J Org Chem

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Supramolecular cavitands that contain inwardly directed functional groups have yielded specialized transformations and trapping of reactive intermediates. A recently reported 3‐wall Au cavitand provides exciting opportunities for supramolecular catalysis. In this study, a variety of substituted γ‐alkynoic acids were reacted to give lactones. The interaction of peripheral “R” groups revealed differential catalyst behavior. Extremely large and small groups reacted with appreciable rate. Intermediate sized groups however, slowed significantly: giving support that size‐specific binding is at play when using cavitands as a scaffold for gold catalysis. These results serve as some of the first evidence of the interplay between substrate and cavitand interior in the catalytic sphere.

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“…They have been added to a very recent review that encompasses earlier mentioned topics as well as the most recent approaches (e.g. using M 12 L 24 cages) …”

Section: Introductionmentioning

confidence: 99%

Au‐Cavitand Catalyzed Alkyne‐Acid Cyclizations

Schramm

2019

Eur J Org Chem

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“…clarity. Selected bond lengths (Å) and angles (°) (only given for the Au1-Au2 cationic part): Au1-P1A 2.236(2); Au1-Cl1A 2.353(2); Au1•••Au2 2.9871(5); P1A-Au1-Cl1A 172.20 (8); P2A-Au2-Cl1A 174.13(9); Au1-Cl1A-Au2 78.79 (7). Complex 3 was amenable to reaction with 1-propynylmagnesium bromide, forming σ,πcoordinated alkynide-bridging complex 5, akin to our previous report using the PN H P iPr scaffold.…”

Section: Resultsmentioning

confidence: 99%

“…The two twin components were related by a twofold axis along the reciprocal vector 0.0001a* + 0.0004b* + 1.0000c*. The BASF scale factor was refined to 0.4368 (7). CCDC 1893603 (5), 1894224 (2), and 1894225 (3) contain the supplementary crystallographic data for this paper (see Supplementary Materials).…”

Section: Single Crystal X-ray Crystallographymentioning

confidence: 99%

“…Recently, the catalytic activation of substrates using two gold centers has proven to be a valuable strategy for a wide range of transformations in the broader context of gold-mediated catalysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Commonly, mono-gold catalysis relies on π-activation of a substrate (e.g., an alkene) by a cationic Au(I) center.…”

Section: Introductionmentioning

confidence: 99%

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Dinuclear Gold Complexes Supported by Wide Bite Angle Diphosphines for Preorganization-Induced Selective Dual-Gold Catalysis

et al. 2019

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The synthesis, reactivity, and potential of well-defined dinuclear gold complexes as precursors for dual-gold catalysis is explored. Using the preorganizing abilities of well-known wide bite angle diphosphine ligands, DBFPhos and DPEPhos, dinuclear Au(I)–Au(I) complexes 1 and 2 are used as precursors to form well-defined monocationic species with either a chlorido- or acetylido-ligand bridging the two gold centers. These compounds are active catalysts for the dual-gold heterocycloaddition of a urea-functionalized alkyne, and the preorganization of both Au-centers affords efficient σ,π-activation of the substrate, even at high dilution, significantly outperforming benchmark mononuclear catalysts.

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“…[33,18] Such sizeselected clusters that comprise of a handful of atoms often display exotic catalytic properties that are much different than that of either nano-sized or bulk catalysts. [59,25,23,67,32,49,51] These clusters contain well-defined number of atoms and offer an ideal platform to study catalysis at the atomic level. They also serve as model systems to enable a comprehensive fundamen-tal insight into the nature of the catalytic processes that are otherwise difficult to explore using catalysts prepared by conventional methods that often yield particles with finite distributions in size and composition.…”

Section: Introductionmentioning

confidence: 99%

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

et al. 2020

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Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (� 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. Here, we introduce an active learning (AL) scheme that trains a NN model on-thefly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (�1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with �500 total reference calculations. Using an extensive DFT test set of~1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/ Å of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

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Gold Catalysis in (Supra)Molecular Cages to Control Reactivity and Selectivity

Cited by 72 publications

References 65 publications

Au‐Cavitand Catalyzed Alkyne‐Acid Cyclizations

Au‐Cavitand Catalyzed Alkyne‐Acid Cyclizations

Dinuclear Gold Complexes Supported by Wide Bite Angle Diphosphines for Preorganization-Induced Selective Dual-Gold Catalysis

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

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