@Article{icces.2024.012552,
AUTHOR = {Jing Wu, E Zhou, An Huang, Hongbin Zhang, Ming Hu, Guangzhao Qin},
TITLE = {Deep-Potential Enabled Multiscale Simulation of Interfacial Thermal Transport in Boron Arsenide Heterostructures},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {32},
YEAR = {2024},
NUMBER = {3},
PAGES = {1--2},
URL = {http://www.techscience.com/icces/v32n3/58896},
ISSN = {1933-2815},
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<sup>-2</sup>K<sup>-1</sup> 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].},
DOI = {10.32604/icces.2024.012552}
}