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<p>This study investigates the thermal transport and mechanical properties of Mg<sub>3</sub>Sb<sub>2</sub>, 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. Mg<sub>3</sub>Sb<sub>2</sub> 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年4月14日

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  • Jiaojiao Cheng,
  • Cunjie Duan,
  • Yunzhen Du,
  • Jizheng Duan,
  • Meiling Qi,
  • Yanwei Chen,
  • Lei Yang,
  • Wenshan Duan,
  • Sheng Zhang,
  • Ping Lin

摘要

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.