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Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

Author

Listed:
  • Cameron J. Owen

    (Harvard University
    Harvard University)

  • Yu Xie

    (Harvard University)

  • Anders Johansson

    (Harvard University)

  • Lixin Sun

    (Harvard University
    Microsoft Research)

  • Boris Kozinsky

    (Harvard University
    Robert Bosch LLC Research and Technology Center)

Abstract

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-‘Herringbone,’ Au(110)-(1 × 2)-‘Missing-Row,’ and Au(100)-‘Quasi-Hexagonal’ reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies.

Suggested Citation

  • Cameron J. Owen & Yu Xie & Anders Johansson & Lixin Sun & Boris Kozinsky, 2024. "Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48192-6
    DOI: 10.1038/s41467-024-48192-6
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    References listed on IDEAS

    as
    1. Albert Musaelian & Simon Batzner & Anders Johansson & Lixin Sun & Cameron J. Owen & Mordechai Kornbluth & Boris Kozinsky, 2023. "Learning local equivariant representations for large-scale atomistic dynamics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Claudio Zeni & Kevin Rossi & Theodore Pavloudis & Joseph Kioseoglou & Stefano de Gironcoli & Richard E. Palmer & Francesca Baletto, 2021. "Data-driven simulation and characterisation of gold nanoparticle melting," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    3. Jonathan Vandermause & Yu Xie & Jin Soo Lim & Cameron J. Owen & Boris Kozinsky, 2022. "Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Nicholas Marcella & Jin Soo Lim & Anna M. Płonka & George Yan & Cameron J. Owen & Jessi E. S. Hoeven & Alexandre C. Foucher & Hio Tong Ngan & Steven B. Torrisi & Nebojsa S. Marinkovic & Eric A. Stach , 2022. "Decoding reactive structures in dilute alloy catalysts," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
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