Deep neural network-based molecular dynamics simulations for AlxGa1-xN alloys and their thermal properties

Abstract Efficient heat dissipation is crucial for the performance and lifetime of high electron mobility transistors (HEMTs). The thermal conductivity of materials and interfacial thermal conductance (ITC) play significant roles in their heat dissipation. To predict the thermal properties of AlxGa1-xN and the ITC of GaN/AlxGa1-xN in HEMTs, a dataset with first-principles accuracy was constructed using concurrent learning method and trained to obtain an interatomic potential employing deep neural networks (DNN) method. Using obtained DNN interatomic potential, equilibrium molecular dynamics simulations were employed to calculate the thermal conductivity of AlxGa1-xN, which showed excellent consistent with experimental results. Additionally, the phonon density of states of AlxGa1-xN and the ITC of GaN/AlxGa1-xN were calculated. Our study revealed a decrease in the ITC of GaN/AlxGa1-xN with increasing x, and the insertion of 1nm-thick AlN at the interface significantly reduced the ITC. This work provided a high-fidelity DNN potential for molecular dynamics simulations of AlxGa1-xN, offering valuable guidance for exploring the thermal transport of complex alloy and heterostructure.