Neural network potential model molecular dynamics study on the effect of temperature and pressure on the lattice thermal conductivity of Mg3Sb2

Abstract

This study investigates the thermal transport and mechanical properties of Mg₃Sb₂, through molecular dynamics (MD) simulations with a neural network potential (NNP) model constructed by machine learning. The model’s computational results align closely with experimental data and Density Functional Theory (DFT) analyses. Mg₃Sb₂ exhibits nearly isotropic thermal conductivity, which decreases with increasing temperature, in line with typical phonon scattering behavior. Additionally, the study explores the effects of pressure on thermal conductivity and structural parameters, revealing that as pressure increases, the volume of the material decreases, leading to directional variations in thermal conductivity. The findings demonstrate the reliability and accuracy of the NNP in predicting material performance.