First-principles based deep neural network force field for molecular dynamics simulation of N–Ga–Al semiconductors

Abstract

Accurate interatomic force fields are of paramount importance for molecular dynamics simulations to explore the thermal transport at the GaN/AlN heterogenous interface, which is a key factor hindering heat dissipation and limiting the performance of GaN power electronic devices. In this work, an interatomic potential (force field) based on a deep neural network technique and first-principles calculations is developed for N–Ga–Al semiconductors to predict the elastic and thermodynamic properties. Using our deep neural network potential (NNP), the precise structural features, elastic constants, and thermal conductivities of GaN, AlN, and their alloy are obtained, which are well consistent with those from experiments and first-principles calculations. The interfacial thermal conductance of GaN/AlN heterostructures with different interfacial morphologies are further studied using molecular dynamics simulations with the NNP. It is found that atomic interdiffusion and disorder at the interfaces dramatically reduces the interfacial thermal conductance. The developed NNP exhibits a larger effective dimension with respect to classical empirical potentials and reaches competitive performances, thus pointing towards attractive advantages in the study of GaN heterostructures and devices with the NNP.