Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential

authored by: Ali Rajabpour, Bohayra Mortazavi, Pedram Mirchi, Julien El Hajj, Yangyu Guo, Xiaoying Zhuang, Samy Merabia
Abstract: Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamond using a computationally efficient machine learning (ML) interatomic potential trained on density functional theory (DFT) data. Using non-equilibrium molecular dynamics (NEMD) simulations, we compute the interfacial thermal conductance (ITC) for various system sizes. Our results reveal an extremely close agreement with experimental data than those obtained using traditional semi-empirical potentials such as Tersoff and Brenner which overestimate ITC. In addition, we analyze the frequency-dependent heat transfer spectrum, providing insights into the contributions of different phonon modes to the interfacial thermal conductance. The ML potential accurately captures the phonon dispersion relations and lifetimes, in good agreement with DFT calculations and experimental observations. It is shown that the Tersoff potential predicts higher phonon group velocities and phonon lifetimes compared to the DFT results. Furthermore, it predicts higher interfacial bonding strength, which is consistent with higher interfacial thermal conductance as compared to the ML potential. This study highlights the use of ML interatomic potentials to improve the accuracy and computational efficiency of thermal transport simulations of complex material interface systems.
Organisation(s): Institute of Photonics
External Organisation(s): Imam Khomeini International University
Université Claude Bernard Lyon 1
Harbin Institute of Technology
Type: Article
Journal: International Journal of Thermal Sciences
Volume: 214
No. of pages: 10
ISSN: 1290-0729
Publication date: 08.2025
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Condensed Matter Physics, General Engineering
Electronic version(s): https://doi.org/10.1016/j.ijthermalsci.2025.109876 (Access: Open)
https://doi.org/10.48550/arXiv.2407.15404 (Access: Open)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{2d41001b4e464701bc90154a82460fa6,
title = "Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential",
abstract = "Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamond using a computationally efficient machine learning (ML) interatomic potential trained on density functional theory (DFT) data. Using non-equilibrium molecular dynamics (NEMD) simulations, we compute the interfacial thermal conductance (ITC) for various system sizes. Our results reveal an extremely close agreement with experimental data than those obtained using traditional semi-empirical potentials such as Tersoff and Brenner which overestimate ITC. In addition, we analyze the frequency-dependent heat transfer spectrum, providing insights into the contributions of different phonon modes to the interfacial thermal conductance. The ML potential accurately captures the phonon dispersion relations and lifetimes, in good agreement with DFT calculations and experimental observations. It is shown that the Tersoff potential predicts higher phonon group velocities and phonon lifetimes compared to the DFT results. Furthermore, it predicts higher interfacial bonding strength, which is consistent with higher interfacial thermal conductance as compared to the ML potential. This study highlights the use of ML interatomic potentials to improve the accuracy and computational efficiency of thermal transport simulations of complex material interface systems.",
author = "Ali Rajabpour and Bohayra Mortazavi and Pedram Mirchi and {El Hajj}, Julien and Yangyu Guo and Xiaoying Zhuang and Samy Merabia",
note = "Publisher Copyright: {\textcopyright} 2025 The Author(s)",
year = "2025",
month = aug,
doi = "10.1016/j.ijthermalsci.2025.109876",
language = "English",
volume = "214",
journal = "International Journal of Thermal Sciences",
issn = "1290-0729",
publisher = "Elsevier Masson",
}

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