ACS Publications. Most Trusted. Most Cited. Most Read
Hydrocarbon Species on the Cu(111) Surface Studied with a Neural Network Potential
My Activity

Figure 1Loading Img
    C: Physical Properties of Materials and Interfaces

    Hydrocarbon Species on the Cu(111) Surface Studied with a Neural Network Potential
    Click to copy article linkArticle link copied!

    • Sen Xu
      Sen Xu
      Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
      More by Sen Xu
    • Liling Wu
      Liling Wu
      Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
      More by Liling Wu
    • Yi Fan
      Yi Fan
      Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
      More by Yi Fan
    • Yufeng Liu
      Yufeng Liu
      Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
      More by Yufeng Liu
    • Xiongzhi Zeng*
      Xiongzhi Zeng
      Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
      *Email: [email protected]
    • Zhenyu Li*
      Zhenyu Li
      Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei 230026, China
      *Email: [email protected]
      More by Zhenyu Li
    Other Access OptionsSupporting Information (1)

    The Journal of Physical Chemistry C

    Cite this: J. Phys. Chem. C 2024, 128, 13, 5697–5707
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpcc.3c08138
    Published March 20, 2024
    Copyright © 2024 American Chemical Society

    Abstract

    Click to copy section linkSection link copied!
    Abstract Image

    Hydrocarbon species are involved in various surface processes, such as graphene growth on Cu surfaces. A systematic examination of their structures, stability, and reactivity from first principles is essential for understanding the atomic mechanisms of these processes. However, this approach has a substantial computational cost. In this study, we train an accurate Cu–C–H neural network (NN) potential using a homemade deep potential learning platform, DPTorch, which exhibits good linear scalability over thousands of CPU cores and multiple GPUs. The obtained NN potential can accurately reproduce the reaction energy and activation barrier for elementary reactions with hydrocarbon species involved. With such an accurate NN potential, the stochastic surface walking global optimization algorithm is then used to explore stable hydrocarbon structures on the Cu(111) surface. It turns out that hydrogen plays an important role in stabilizing small carbon ring structures. Free energy surfaces are constructed via enhanced sampling using NN potential-based molecular dynamics simulations, which gives a revision to statistically not well-converged free energy barriers predicted previously using ab initio molecular dynamics. At the same time, new pathways are found for both CH and C2 dissociation reactions. These results provide valuable insights into the chemistry of hydrocarbons on the Cu surface.

    Copyright © 2024 American Chemical Society

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. Add or change your institution or let them know you’d like them to include access.

    Supporting Information

    Click to copy section linkSection link copied!

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcc.3c08138.

    • Features and performance of DPTorch, training strategy, composition of the training set, benchmark, analysis of the NNMD trajectories, and comparison of vdW methods (PDF)

    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    Click to copy section linkSection link copied!
    Citation Statements
    Explore this article's citation statements on scite.ai

    This article is cited by 1 publications.

    1. Xiucai Sun, Shuang Lou, Weizhi Wang, Xuqin Liu, Xiaoli Sun, Yuqing Song, Weimin Yang, Zhongfan Liu. Kinetics of hydrogen constrained graphene growth on Cu substrate. Nano Research 2024, 17 (11) , 9284-9292. https://doi.org/10.1007/s12274-024-6945-2

    The Journal of Physical Chemistry C

    Cite this: J. Phys. Chem. C 2024, 128, 13, 5697–5707
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpcc.3c08138
    Published March 20, 2024
    Copyright © 2024 American Chemical Society

    Article Views

    649

    Altmetric

    -

    Citations

    Learn about these metrics

    Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

    Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

    The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.