Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 109876 |
| Seitenumfang | 10 |
| Fachzeitschrift | International Journal of Thermal Sciences |
| Jahrgang | 214 |
| Frühes Online-Datum | 18 März 2025 |
| Publikationsstatus | Veröffentlicht - Aug. 2025 |
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.
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- Physik und Astronomie (insg.)
- Physik der kondensierten Materie
- Ingenieurwesen (insg.)
- Allgemeiner Maschinenbau
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in: International Journal of Thermal Sciences, Jahrgang 214, 109876, 08.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential
AU - Rajabpour, Ali
AU - Mortazavi, Bohayra
AU - Mirchi, Pedram
AU - El Hajj, Julien
AU - Guo, Yangyu
AU - Zhuang, Xiaoying
AU - Merabia, Samy
N1 - Publisher Copyright: © 2025 The Author(s)
PY - 2025/8
Y1 - 2025/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=105000050763&partnerID=8YFLogxK
U2 - 10.1016/j.ijthermalsci.2025.109876
DO - 10.1016/j.ijthermalsci.2025.109876
M3 - Article
AN - SCOPUS:105000050763
VL - 214
JO - International Journal of Thermal Sciences
JF - International Journal of Thermal Sciences
SN - 1290-0729
M1 - 109876
ER -