Thermal transport in disordered wurtzite ScAlN alloys using machine learning interatomic potentials

Disordered wurtzite ScAlN alloy is an emerging material with a strong piezoelectric coefficient that promotes its application in 5 G communication devices. To extend the service lifetime of the device, it is an urgent task to understand the thermal transport properties of this alloy for thermal management. Yet a thorough understanding of the thermal transport in the disordered ScAlN alloy is hindered by the inaccuracy of empirical interatomic potentials adopted for atomistic simulations. Herein, we developed an accurate and efficient neuro-evolution potential and combined it with Allen and Feldman theory and molecular dynamics to investigate thermal transport properties of disordered ScAlN alloys with varying ratios. This potential is shown to have the same computational accuracy as first principles by comparing the phonon spectrum and radial distribution function. Based on the homogeneous non-equilibrium molecular dynamics simulations, the lattice thermal conductivity of the disordered alloys reaches its maximum value (less than 12 W/m & sdot;K) at room temperature. Further investigation of the temperature effects on the vibrational modes shows that it affects the mode lifetimes for the propagons and the mode heat capacities for the diffusions. In addition, it was found that both mode lifetime and diffusivity decrease with increasing Sc concentration. This work aims to train a universal machine learning interatomic potential of disordered ScAlN alloys, seeking to accelerate the thermal management design of related devices.