RESUMO

The presence of vacancy defects significantly impacts thermal properties of materials. In this research, we delve into the effects of vacancy defects on the thermal conductivity of ternary alloy BaAgBi, employing molecular dynamics simulations coupled with a deep neural network potential (NNP). Initially, we validate the precision of our NNP by comparing their predictions for energy, atomic forces, phonon dispersion curves, phonon density of states, and vacancy formation energy with density functional theory calculations, ensuring a high degree of accuracy. Our findings reveal that the reduction in thermal conductivity due to vacancies aligns with the Debye-Callaway model, with variations depending on the type of vacancy. Specifically, Ba vacancies result in the most notable decrement in thermal conductivity, attributable to their low phonon participation ratio and high lattice distortion, both factors that enhance phonon scattering. Besides, we find that the high energy barrier (∼1.66 eV) indicates that Ba vacancies hardly migrate at 300 K. This study helps us understand how vacancies affect thermal conductivity in BaAgBi and how different vacancy types affect it.