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Deep-Potential Enabled Multiscale Simulation of Interfacial Thermal Transport in Boron Arsenide Heterostructures

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1 State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical, Hunan University, Changsha, 410082, China
2 Institut für Materialwissenschaft, Technische Universität Darmstadt, Darmstadt, 64289, Germany
3 Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA

* Corresponding Author: Guangzhao Qin. Email:

The International Conference on Computational & Experimental Engineering and Sciences 2024, 32(3), 1-2. https://doi.org/10.32604/icces.2024.012552

Abstract

High thermal conductivity substrate plays a significant role for efficient heat dissipation of electronic devices, and it is urgent to optimize the interfacial thermal resistance. As a novel material with ultra-high thermal conductivity second only to diamond, boron arsenide (BAs) shows promising applications in electronics cooling [1,2]. By adopting multi-scale simulation method driven by machine learning potential, we systematically study the thermal transport properties of boron arsenide, and further investigate the interfacial thermal transport in the GaN-BAs heterostructures. Ultrahigh interfacial thermal conductance of 260 MW m^-2K^-1 is achieved, which agrees well with experimental measurements, and the fundamental mechanism is found lying in the well-matched lattice vibrations of BAs and GaN [1,3,4]. Moreover, the competition between grain size and boundary resistance was revealed with size increasing from 1 nm to 100 m. The results are expected to lay theoretical foundation for the applications of BAs in advanced thermal management of electronic devices [5].

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