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    Zhicong Wei, Haoqiang Li, Jianlian Huang, Weikuang Li, Yijuan Li, Yajuan Cheng, Shiyun Xiong. Finite-size effects on phonon-mediated thermal transport across Si–Ge interfaces: Spectral analysis and parameter optimization for molecular dynamics simulationsJ. Chin. Phys. B, 2025, 34(11): 116301.
    Zhicong Wei, Haoqiang Li, Jianlian Huang, Weikuang Li, Yijuan Li, Yajuan Cheng, Shiyun Xiong. Finite-size effects on phonon-mediated thermal transport across Si–Ge interfaces: Spectral analysis and parameter optimization for molecular dynamics simulationsJ. Chin. Phys. B, 2025, 34(11): 116301.
  • Finite-size effects on phonon-mediated thermal transport across Si–Ge interfaces: Spectral analysis and parameter optimization for molecular dynamics simulations

    • The interfacial thermal resistance (ITR) at material interfaces has emerged as a critical factor in the thermal management of micro/nanoelectronic devices and composite materials. Using non-equilibrium molecular dynamics simulations, we systematically investigate how simulation parameters affect the calculated ITR in Si/Ge heterojunctions. Our results demonstrate that the ITR decreases with increasing system length Lsys and thermal bath length Lbath. We identify linear relationships between ITR and the inverse of both Lsys and Lbath, enabling reliable extrapolation to infinite-system values. While the thermostat coupling constant τ shows a negligible influence on ITR, excessively large values (τ > 5 ps) compromise temperature control accuracy. Spectral analysis reveals that these size effects primarily originate from mid-to-low-frequency phonons (< 6 THz), whose long mean free paths make their transport particularly sensitive to system dimensions. This work establishes fundamental guidelines for parameter selection in interfacial thermal transport simulations, while providing new insights into phonon–interface interactions. The findings offer valuable implications for thermal design in high-power devices and composite materials, where accurate ITR prediction is crucial for performance optimization.
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