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Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface

Author

Listed:
  • Di Zhang

    (Shanghai Jiao Tong University
    Tohoku University)

  • Peiyun Yi

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Xinmin Lai

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Linfa Peng

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Hao Li

    (Tohoku University)

Abstract

Substrate-catalyzed growth offers a highly promising approach for the controlled synthesis of carbon nanostructures. However, the growth mechanisms on dynamic catalytic surfaces and the development of more general design strategies remain ongoing challenges. Here we show how an active machine-learning model effectively reveals the microscopic processes involved in substrate-catalyzed growth. Utilizing a synergistic approach of molecular dynamics and time-stamped force-biased Monte Carlo methods, augmented by the Gaussian Approximation Potential, we perform fully dynamic simulations of graphene growth on Cu(111). Our findings accurately replicate essential subprocesses–from the preferred diffusion of carbon monomer/dimer, chain or ring formations to edge-passivated Cu-aided graphene growth and bond breaks by ion impacts. Extending our simulations to carbon deposition on metal surfaces like Cu(111), Cr(110), Ti(001), and oxygen-contaminated Cu(111), our results align closely with experimental observations, providing a practical and efficient approach for designing metallic or alloy substrates to achieve desired carbon nanostructures and explore further reaction possibilities.

Suggested Citation

  • Di Zhang & Peiyun Yi & Xinmin Lai & Linfa Peng & Hao Li, 2024. "Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44525-z
    DOI: 10.1038/s41467-023-44525-z
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    References listed on IDEAS

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    1. Volker L. Deringer & Noam Bernstein & Gábor Csányi & Chiheb Mahmoud & Michele Ceriotti & Mark Wilson & David A. Drabold & Stephen R. Elliott, 2021. "Origins of structural and electronic transitions in disordered silicon," Nature, Nature, vol. 589(7840), pages 59-64, January.
    2. Muhong Wu & Zhibin Zhang & Xiaozhi Xu & Zhihong Zhang & Yunrui Duan & Jichen Dong & Ruixi Qiao & Sifan You & Li Wang & Jiajie Qi & Dingxin Zou & Nianze Shang & Yubo Yang & Hui Li & Lan Zhu & Junliang , 2020. "Seeded growth of large single-crystal copper foils with high-index facets," Nature, Nature, vol. 581(7809), pages 406-410, May.
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