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DFT Variants for Mixed-Metal Oxides. Benchmarks Using Multi-Center Cluster Models
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    DFT Variants for Mixed-Metal Oxides. Benchmarks Using Multi-Center Cluster Models
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    • Graham Rugg
      Graham Rugg
      Institute of High Performance Computing, Agency for Science, Technology and Research, 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore
      More by Graham Rugg
    • Alexander Genest
      Alexander Genest
      Institute of High Performance Computing, Agency for Science, Technology and Research, 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore
    • Notker Rösch*
      Notker Rösch
      Institute of High Performance Computing, Agency for Science, Technology and Research, 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore
      Department Chemie and Catalysis Research Center, Technische Universität München, 85747 Garching, Germany
      *(N.R.) E-mail: [email protected]
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    The Journal of Physical Chemistry A

    Cite this: J. Phys. Chem. A 2018, 122, 35, 7042–7050
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    https://doi.org/10.1021/acs.jpca.8b05331
    Published August 24, 2018
    Copyright © 2018 American Chemical Society

    Abstract

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    Mixed-metal oxides, e.g., V–Mo and Bi–Mo, are promising selective oxidation catalysts. Yet, their intricate chemical composition and electronic structure often confound DFT methods. This study addresses problems arising from the simultaneous presence of two kinds of transition metals, by probing eight functionals–five hybrid functionals (MN15, M06, PBE0-D3, B3LYP-D3, and TPSSh-D3), the meta-GGA functional M06-L-D3, the range-separated functional ωB97XD, and the GGA functional PBE-D3. We examine the ability of these functionals to localize reducing electrons, and to reproduce reaction energies from CCSD(T) calculations. Accordingly, hybrid functionals containing 20% or more exact exchange perform considerably better in both tests. The B3LYP-D3 approach exhibits the lowest overall mean absolute deviation of reaction energies (OMAD), 21 kJ mol–1, and gave electron distributions as expected from the local lattice structure according to the pseudo-Jahn–Teller effect. MN15 and PBE0-D3 reproduced the electron distributions, but bore slightly higher OMAD values, at 31 and 32 kJ mol–1. Despite acceptable OMAD values, M06 (28 kJ mol–1) and TPSSh (23 kJ mol–1) in some cases did not yield the expected electron distributions. The range-separated functional ωB97XD experienced the opposite problem, yielding correct electron distributions but a poor OMAD of 41 kJ mol–1. M06-L-D3 and PBE-D3 performed relatively poorly, regarding the electron distribution and the OMAD values, 39 and 65 kJ mol–1, respectively.

    Copyright © 2018 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpca.8b05331.

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    Cited By

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    This article is cited by 10 publications.

    1. Alicia Lund, G. V. Manohara, Ah-Young Song, Kevin Maik Jablonka, Christopher P. Ireland, Li Anne Cheah, Berend Smit, Susana Garcia, Jeffrey A. Reimer. Characterization of Chemisorbed Species and Active Adsorption Sites in Mg–Al Mixed Metal Oxides for High-Temperature CO2 Capture. Chemistry of Materials 2022, 34 (9) , 3893-3901. https://doi.org/10.1021/acs.chemmater.1c03101
    2. Juan Manuel Arce-Ramos, Alexander Genest, Notker Rösch. How TeO Defects in the MoVNbTeO Catalyst Material Affect the V4+ Distribution: A Computational Study. The Journal of Physical Chemistry C 2020, 124 (34) , 18628-18638. https://doi.org/10.1021/acs.jpcc.0c05447
    3. WooSeok Jeong, Samuel J. Stoneburner, Daniel King, Ruye Li, Andrew Walker, Roland Lindh, Laura Gagliardi. Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation. Journal of Chemical Theory and Computation 2020, 16 (4) , 2389-2399. https://doi.org/10.1021/acs.jctc.9b01297
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    5. Shannon E. Cooney, S. Genevieve Duggan, M. Rebecca A. Walls, Noah J. Gibson, James M. Mayer, Pere Miro, Ellen M. Matson. Engineering mechanisms of proton-coupled electron transfer to a titanium-substituted polyoxovanadate–alkoxide. Chemical Science 2025, 336 https://doi.org/10.1039/D4SC06468B
    6. Martin Kaupp. Quantum Chemical Approaches to Treat Mixed‐Valence Systems Realistically for Delocalized and Localized Situations. 2023, 93-120. https://doi.org/10.1002/9783527835287.ch3
    7. Abing Duan, Fengjiao Xiao, Yu Lan, Linbin Niu. Mechanistic views and computational studies on transition-metal-catalyzed reductive coupling reactions. Chemical Society Reviews 2022, 51 (24) , 9986-10015. https://doi.org/10.1039/D2CS00371F
    8. Juan Manuel Arce-Ramos, Graham Rugg, Alexander Genest, Notker Rösch. Probing the Positions of TeO Moieties in the Channels of the MoVNbTeO M1 Catalyst: A Density Functional Theory Model Study. Catalysis Letters 2021, 151 (10) , 2884-2893. https://doi.org/10.1007/s10562-021-03538-3
    9. Torstein Fjermestad, Wen-Qing Li, Alexander Genest, Notker Rösch. Configurations of V4+ centers in the MoVO catalyst material. A systematic stability analysis of DFT results. SN Applied Sciences 2020, 2 (11) https://doi.org/10.1007/s42452-020-03686-y
    10. Wen-Qing Li, Torstein Fjermestad, Alexander Genest, Notker Rösch. Reactivity trends of the MoVO x mixed metal oxide catalyst from density functional modeling. Catalysis Science & Technology 2019, 9 (7) , 1559-1569. https://doi.org/10.1039/C8CY02545B

    The Journal of Physical Chemistry A

    Cite this: J. Phys. Chem. A 2018, 122, 35, 7042–7050
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpca.8b05331
    Published August 24, 2018
    Copyright © 2018 American Chemical Society

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