  High-efficient heat dissipation plays critical role for high-power-density
electronics. Experimental synthesis of ultrahigh thermal conductivity boron
arsenide (BAs, 1300 W m-1K-1) cooling substrates into the wide-bandgap
semiconductor of gallium nitride (GaN) devices has been realized. However, the
lack of systematic analysis on the heat transfer across the BAs-GaN interface
hampers the practical applications. In this study, by constructing the accurate
and high-efficient machine learning interatomic potentials, we performed
multiscale simulations of the BAs-GaN heterostructures. Ultrahigh interfacial
thermal conductance (ITC) of 265 MW m-2K-1 is achieved, which lies in the
well-matched lattice vibrations of BAs and GaN. Moreover, the competition
between grain size and boundary resistance was revealed with size increasing
from 1 nm to 100 {\mu}m. Such deep-potential equipped multiscale simulations
not only promote the practical applications of BAs cooling substrates in
electronics, but also offer new approach for designing advanced thermal
management systems.
