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

JJ Cheng and CJ Duan and YZ Du and JZ Duan and ML Qi and YW Chen and L Yang and WS Duan and S Zhang and P Lin, JOURNAL OF MATERIALS SCIENCE, 60, 6551-6564 (2025).

DOI: 10.1007/s10853-025-10755-3

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.

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