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In Journal of physics. Condensed matter : an Institute of Physics journal

As a promising thermoelectric material, tin selenide (SnSe) is of relatively low thermal conductivity. However, the phonon transport mechanisms in SnSe are not fully understood due to the complex phase transition, dynamical instability, and strong anharmonicity. In this work, we perform molecular dynamics simulations with a machine-learning interatomic potential to explore the thermal transport properties of SnSe at different temperatures. The developed interatomic potential is parameterized using the framework of moment tensor potential, exhibiting satisfactory predictions on temperature-dependent lattice constants and phonon dispersion, as well as phase transition temperature. From equilibrium molecular dynamics simulations, we obtained the thermal conductivity tensor from 200 K to 900 K. The origins of temperature-dependent thermal conductivity anisotropy and the roles of four-phonon scatterings are identified. The obtained interatomic potential can be utilized to study the mechanical and thermal properties of SnSe and related nanostructures in a wide range of temperatures.

Liu Huan, Qian Xin, Bao Hua, Zhao Changying, Gu Xiaokun


machine-learning potential, molecular dynamics, thermal conductivity, tin selenide