QM/MM modelling of the TS-1 catalyst using HPCx

To, Judy; Sherwood, Paul; Sokol, Alexey A.; Bush, Ian J.; Catlow, C. Richard A.; Dam, Hubertus J. J. van; French, Samuel A.; Guest, Martyn F.

doi:10.1039/b601089j

Cited by 43 publications

(39 citation statements)

References 47 publications

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“…The CHEMSHELL code is a valuable computational tool for surface chemistry, with applications ranging from astrochemistry to industrial catalysis. 2,3,5,21 In this paper, we study the reactivity of active charged and radical silica surface sites, which are also indicated in the biotoxicity of silica. 6,7,22 The described computational approach could augment recent QM-cluster calculations that study the reactivity of those surface sites 6,22 because the embedding scheme takes account of the structural and electrostatic effect of the surrounding solid.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we chose to use the Hill-Sauer potentials, 25 which have been used for QM/MM studies of zeolites. 2,3,27 However, it was necessary to truncate the complex potentials for use with MARVIN ͑Ref. 28͒ and GULP ͑Ref.…”

Section: B MM Parametersmentioning

confidence: 99%

“…[1][2][3] The division of a large system into a quantum mechanical ͑QM͒ and a molecular mechanical ͑MM͒ region ͑QM/MM͒ allows the reactive center to be studied with a high-level theory, while the effects of the environment ͑structural and electrostatic͒ are calculated at the computationally less-demanding MM level. QM/MM was first developed in the 1970s, 4 and numerous variations of the method have been devised since ͑see Ref.…”

Section: Introductionmentioning

confidence: 99%

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Hydrogenation of CO on a silica surface: An embedded cluster approach

Goumans

Catlow

Brown

2008

The Journal of Chemical Physics

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Hydrogenation of CO on a silica surface: an embedded cluster approach Article (Published Version) http://sro.sussex.ac.uk Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. The sequential addition of H atoms to CO adsorbed on a siliceous edingtonite surface is studied with an embedded cluster approach, using density functional theory for the quantum mechanical ͑QM͒ cluster and a molecular force field for the molecular mechanical ͑MM͒ cluster. With this setup, calculated QM/MM adsorption energies are in agreement with previous calculations employing periodic boundary conditions. The catalytic effect of the siliceous edingtonite ͑100͒ surface on CO hydrogenation is assessed because of its relevance to astrochemistry. While adsorption of CO on a silanol group on the hydroxylated surface did not reduce the activation energy for the reaction with a H atom, a negatively charged defect on the surface is found to reduce the gas phase barriers for the hydrogenation of both CO and H 2 C v O. The embedded cluster approach is shown to be a useful and flexible tool for studying reactions on ͑semi-͒ionic surfaces and specific defects thereon. The methodology presented here could easily be applied to study reactions on silica surfaces that are of relevance to other scientific areas, such as biotoxicity of silica dust and geochemistry.

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Section: Methodsmentioning

confidence: 99%

Section: B MM Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hydrogenation of CO on a silica surface: An embedded cluster approach

Goumans

Catlow

Brown

2008

The Journal of Chemical Physics

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“…Traditionally, the synthesis of titanosilicates has been carried out via techniques such as direct hydrothermal synthesis [60][61][62], the dry gel conversion method [63] and the post-synthesis method [64]. Later on, amorphous varieties became prominent, targeting bulky organic substrates and oxidants [39,65], as opposed to the conventional, ordered, crystalline [66][67][68] types, which were suitable for smaller reactants. Soft templating techniques have been utilized to generate such amorphous titanosilicates along with uniform particle shape and size, and increased surface area and robustness [44,45].…”

Section: Introductionmentioning

confidence: 99%

Optimization of mesoporous titanosilicate catalysts for cyclohexene epoxidation via statistically guided synthesis

et al. 2018

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An efficient approach to improve the catalytic activity of titanosilicates is introduced. The Doehlert matrix (DM) statistical model was utilized to probe the synthetic parameters of mesoporous titanosilicate microspheres (MTSM), in order to increase their catalytic activity with a minimal number of experiments. Synthesis optimization was carried out by varying two parameters simultaneously: homogenizing temperature and surfactant weight. Thirteen different MTSM samples were synthesized in two sequential 'matrices' according to Doehlert conditions and were used to catalyse the epoxidation of cyclohexene with tert-butyl hydroperoxide. The samples (and the corresponding synthesis conditions) with superior catalytic activity in terms of product yield and selectivity were identified. In addition, this approach revealed the limiting values of each synthesis parameter, beyond which the material becomes catalytically ineffective. This study demonstrates that the DM approach can be broadly used as a powerful and time-efficient tool for investigating the optimal synthesis conditions of heterogeneous catalysts. Abbreviation

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“…The QM/MM method is now widely used to model biomolecular systems [65–67], as well as other systems such as inorganic/organometallic [68,69], solid-state [70,71] and explicit solvent systems [72,73]. Moreover, combined with X-ray crystallography or NMR, QM/MM methods are also useful in the refinement of protein structures [74–77].…”

Section: Computational Toolsmentioning

confidence: 99%

Investigation of Structural Dynamics of Enzymes and Protonation States of Substrates Using Computational Tools

et al. 2016

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This review discusses the use of molecular modeling tools, together with existing experimental findings, to provide a complete atomic-level description of enzyme dynamics and function. We focus on functionally relevant conformational dynamics of enzymes and the protonation states of substrates. The conformational fluctuations of enzymes usually play a crucial role in substrate recognition and catalysis. Protein dynamics can be altered by a tiny change in a molecular system such as different protonation states of various intermediates or by a significant perturbation such as a ligand association. Here we review recent advances in applying atomistic molecular dynamics (MD) simulations to investigate allosteric and network regulation of tryptophan synthase (TRPS) and protonation states of its intermediates and catalysis. In addition, we review studies using quantum mechanics/molecular mechanics (QM/MM) methods to investigate the protonation states of catalytic residues of β-Ketoacyl ACP synthase I (KasA). We also discuss modeling of large-scale protein motions for HIV-1 protease with coarse-grained Brownian dynamics (BD) simulations.

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QM/MM modelling of the TS-1 catalyst using HPCx

Cited by 43 publications

References 47 publications

Hydrogenation of CO on a silica surface: An embedded cluster approach

Hydrogenation of CO on a silica surface: An embedded cluster approach

Optimization of mesoporous titanosilicate catalysts for cyclohexene epoxidation via statistically guided synthesis

Investigation of Structural Dynamics of Enzymes and Protonation States of Substrates Using Computational Tools

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