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Machine-Learning Approach for the Development of Structure–Energy Relationships of ZnO Nanoparticles

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    Machine-Learning Approach for the Development of Structure–Energy Relationships of ZnO Nanoparticles
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    The Journal of Physical Chemistry C

    Cite this: J. Phys. Chem. C 2018, 122, 32, 18621–18639
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    https://doi.org/10.1021/acs.jpcc.8b01667
    Published July 20, 2018
    Copyright © 2018 American Chemical Society
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    Abstract

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    The structure–energy relationships for the zinc oxide morphologies were investigated using a newly developed fragment-based energy decomposition approach. In this approach, the local chemical compositions of a material are abstracted as fragment types that serve as the material’s genes with respect to its thermodynamic properties. A machine learning-based fragment recognition scheme was developed to learn about the fragment-related knowledge from a relatively small training set consisting of computationally viable ultrasmall nanoparticles. The knowledge gained including the fragment geometries and fragment energy parameters can be used for the classification and energy expression of the test sets consisting of different polymorphs and morphologies of that material at various scales. The stabilities of ZnO nanoparticles with different morphologies were expressed explicitly as functions of the particle size. The size-related phase transitions among various morphologies including wurtzite prisms, wurtzite octahedrons, body-centered tetragonal particles, sodalite-like particles, single-layered cages, multilayered cages, and nonpolar hexagonal prisms were predicted. The multilayered cages with nonpolar surfaces exhibit superior stability among the low-energy morphologies, but wurtzite nanoparticles are more favorable under practical synthesis and growth conditions under the control of the kinetics. The growth mechanism for ZnO clusters, ultrasmall nanoparticles, nanocrystals, and bulk-sized particles is proposed based on the synergy between the size-related phase transitions and external factors that affect the surface energies of the particles. Our interpretation of the Wulff theorem at a fragment-sized resolution provides new chemical insight for understanding the structural phase transition and particle growth for ZnO at various scales.

    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.jpcc.8b01667.

    • Complete author lists for ref (25); rationales for the use of frozen cuts of WZ geometries instead of relaxed geometries for FBED; conversion between the fragment-based surface energy density and the conventional surface energy density for the low-index surfaces of WZ; prediction of the IARs for NHP and BCT morphologies; relaxation of the WZ USNP to form the NHP USNP; the calculated ZPE and thermal corrections NCEP for low-energy (ZnO)n clusters; fragment types recognized for ensemble-W; fragment types recognized for ensemble-U; fitting errors in NCEs (FBED) for ensemble-W and ensemble-U with respect to NCEs (DFT) from the explicit calculations; schematic atomistic structures for MC-GD and WZ-GD (ZnO)168 structures; and Cartesian coordinates with the fragmentation assignments for structures of ensemble-W (unoptimized) and ensemble-U (optimized) (PDF)

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    Cited By

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    This article is cited by 13 publications.

    1. Yifan Wang, Ya-Qiong Su, Emiel J. M. Hensen, Dionisios G. Vlachos. Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization. ACS Nano 2020, 14 (10) , 13995-14007. https://doi.org/10.1021/acsnano.0c06472
    2. Mingyang Chen, Ashley S. McNeill, Yiqin Hu, David A. Dixon. Elucidation of Bottom-Up Growth of CaCO3 Involving Prenucleation Clusters from Structure Predictions and Decomposition of Globally Optimized (CaCO3)n Nanoclusters. ACS Nano 2020, 14 (4) , 4153-4165. https://doi.org/10.1021/acsnano.9b08907
    3. Danilo González, Bruno Camino, Javier Heras-Domingo, Albert Rimola, Luis Rodríguez-Santiago, Xavier Solans-Monfort, Mariona Sodupe. BCN-M: A Free Computational Tool for Generating Wulff-like Nanoparticle Models with Controlled Stoichiometry. The Journal of Physical Chemistry C 2020, 124 (1) , 1227-1237. https://doi.org/10.1021/acs.jpcc.9b10506
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    6. Mausumi Ray, Biswajit Saha, Tapan Kumar Rout, Amar Nath Bhagat. Reactivity of organogermanes with ZnO substrate. Journal of Organometallic Chemistry 2024, 1006 , 123002. https://doi.org/10.1016/j.jorganchem.2023.123002
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    10. Dejan Zagorac, J. Christian Schön. Energy landscapes of pure and doped ZnO: from bulk crystals to nanostructures. 2022, 151-193. https://doi.org/10.1016/B978-0-12-824406-7.00015-4
    11. Basheer Aazaad, Senthilkumar Lakshmipathi. ZnO and TiO 2 clusters as catalyst in the addition and abstraction reaction of acrylic acid with the OH radical. International Journal of Chemical Kinetics 2020, 52 (1) , 3-15. https://doi.org/10.1002/kin.21324
    12. Geun Ho Gu, Juhwan Noh, Inkyung Kim, Yousung Jung. Machine learning for renewable energy materials. Journal of Materials Chemistry A 2019, 7 (29) , 17096-17117. https://doi.org/10.1039/C9TA02356A
    13. Mingyang Chen. Structures and evolution of metal oxide nanoclusters: Bottom-up genetic algorithm and fragment-based energy decomposition model. 2019, 105-169. https://doi.org/10.1016/bs.arcc.2019.08.001
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    The Journal of Physical Chemistry C

    Cite this: J. Phys. Chem. C 2018, 122, 32, 18621–18639
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpcc.8b01667
    Published July 20, 2018
    Copyright © 2018 American Chemical Society
    Request reuse permissions

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