Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter

Sun, Zhanpeng; Qi, Zijun; Song, Yunfei; Li, Lijie; Liu, Sheng; Shen, Wei; Wu, Gai

doi:10.1038/s41524-026-02007-y

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Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter

Zhanpeng Sun, Zijun Qi, Yunfei Song, Lijie Li

, Sheng Liu, Wei Shen, Gai Wu

npj Computational Materials, Volume: 12, Start page: 130

Swansea University Author: Lijie Li

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DOI (Published version): 10.1038/s41524-026-02007-y

Abstract

The rising power density of advanced electronics demands improved thermal management, while traditional single-scale methods are unable to fully reveal the complex heat transfer mechanisms in heterostructures. This work establishes a multiscale simulation framework by constructing a machine learning...

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Published in:	npj Computational Materials
ISSN:	2057-3960
Published:	Springer Nature 2026
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa71466

first_indexed	2026-02-19T10:55:38Z
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This work establishes a multiscale simulation framework by constructing a machine learning potential, enabling accurate cross-scale parameter transfer from atomic to mesoscopic and then to macroscopic levels. Results show that the thermal boundary resistance (TBR) at the β-Ga2O3/diamond interface is higher than that at the β-Ga2O3/Si and β-Ga2O3/SiC interfaces, and that the TBR decreases with increasing temperature, which contradicts conventional understanding. Vibrational density of states and interface conductance modal analysis elucidate the underlying mechanisms. These mesoscale insights are incorporated into macroscopic simulations, showing the β-Ga2O3/diamond heterostructure’s peak power capability reaches 226% of that of β-Ga2O3/Si. Further analysis reveals that although the thermal conductivity of the heat-spreading substrate remains the dominant factor in overall thermal performance, the thermal bottleneck gradually shifts toward the interface as both substrate conductivity and operating temperature rise. Moreover, crystal orientation significantly influences thermal performance and thermal stress distribution, necessitating careful trade-offs. 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spelling	2026-04-28T13:01:24.8020308 v2 71466 2026-02-19 Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter ed2c658b77679a28e4c1dcf95af06bd6 0000-0003-4630-7692 Lijie Li Lijie Li true false 2026-02-19 ACEM The rising power density of advanced electronics demands improved thermal management, while traditional single-scale methods are unable to fully reveal the complex heat transfer mechanisms in heterostructures. This work establishes a multiscale simulation framework by constructing a machine learning potential, enabling accurate cross-scale parameter transfer from atomic to mesoscopic and then to macroscopic levels. Results show that the thermal boundary resistance (TBR) at the β-Ga2O3/diamond interface is higher than that at the β-Ga2O3/Si and β-Ga2O3/SiC interfaces, and that the TBR decreases with increasing temperature, which contradicts conventional understanding. Vibrational density of states and interface conductance modal analysis elucidate the underlying mechanisms. These mesoscale insights are incorporated into macroscopic simulations, showing the β-Ga2O3/diamond heterostructure’s peak power capability reaches 226% of that of β-Ga2O3/Si. Further analysis reveals that although the thermal conductivity of the heat-spreading substrate remains the dominant factor in overall thermal performance, the thermal bottleneck gradually shifts toward the interface as both substrate conductivity and operating temperature rise. Moreover, crystal orientation significantly influences thermal performance and thermal stress distribution, necessitating careful trade-offs. This study not only provides effective strategies for optimizing β-Ga2O3-based devices but also establishes a generalizable paradigm for cross-scale thermal management research in heterogeneous material systems. Journal Article npj Computational Materials 12 130 Springer Nature 2057-3960 18 2 2026 2026-02-18 10.1038/s41524-026-02007-y COLLEGE NANME Aerospace Civil Electrical and Mechanical Engineering COLLEGE CODE ACEM Swansea University Another institution paid the OA fee This work was funded by the National Natural Science Foundation of China (Grant Nos. 92473102, 52202045, 62004141), the Shenzhen Science and Technology Program (Grant No. JCYJ20240813175906008), the State Key Laboratory of Micro-nano Engineering Science (Grant No. MES202608), and the Open Fund of Hubei Key Laboratory of Electronic Manufacturing and Packaging Integration (Wuhan University) (Grant No. EMPI2025007). 2026-04-28T13:01:24.8020308 2026-02-19T10:53:05.7287149 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Zhanpeng Sun 1 Zijun Qi 2 Yunfei Song 3 Lijie Li 0000-0003-4630-7692 4 Sheng Liu 5 Wei Shen 6 Gai Wu 7 71466__36625__0de7fd923ac84c9db0303dcec012ade1.pdf 71466.VOR.pdf 2026-04-28T12:58:18.9682244 Output 6731701 application/pdf Version of Record true © The Author(s) 2026. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. true eng
title	Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter
spellingShingle	Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter Lijie Li
title_short	Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter
title_full	Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter
title_fullStr	Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter
title_full_unstemmed	Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter
title_sort	Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter
author_id_str_mv	ed2c658b77679a28e4c1dcf95af06bd6
author_id_fullname_str_mv	ed2c658b77679a28e4c1dcf95af06bd6_***_Lijie Li
author	Lijie Li
author2	Zhanpeng Sun Zijun Qi Yunfei Song Lijie Li Sheng Liu Wei Shen Gai Wu
format	Journal article
container_title	npj Computational Materials
container_volume	12
container_start_page	130
publishDate	2026
institution	Swansea University
issn	2057-3960
doi_str_mv	10.1038/s41524-026-02007-y
publisher	Springer Nature
college_str	Faculty of Science and Engineering
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department_str	School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
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description	The rising power density of advanced electronics demands improved thermal management, while traditional single-scale methods are unable to fully reveal the complex heat transfer mechanisms in heterostructures. This work establishes a multiscale simulation framework by constructing a machine learning potential, enabling accurate cross-scale parameter transfer from atomic to mesoscopic and then to macroscopic levels. Results show that the thermal boundary resistance (TBR) at the β-Ga2O3/diamond interface is higher than that at the β-Ga2O3/Si and β-Ga2O3/SiC interfaces, and that the TBR decreases with increasing temperature, which contradicts conventional understanding. Vibrational density of states and interface conductance modal analysis elucidate the underlying mechanisms. These mesoscale insights are incorporated into macroscopic simulations, showing the β-Ga2O3/diamond heterostructure’s peak power capability reaches 226% of that of β-Ga2O3/Si. Further analysis reveals that although the thermal conductivity of the heat-spreading substrate remains the dominant factor in overall thermal performance, the thermal bottleneck gradually shifts toward the interface as both substrate conductivity and operating temperature rise. Moreover, crystal orientation significantly influences thermal performance and thermal stress distribution, necessitating careful trade-offs. This study not only provides effective strategies for optimizing β-Ga2O3-based devices but also establishes a generalizable paradigm for cross-scale thermal management research in heterogeneous material systems.
published_date	2026-02-18T07:56:06Z
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Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter

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