High-temperature phonon transport properties of SnSe from machine-learning interatomic potential

Area of Science:

• Materials Science
• Condensed Matter Physics
• Computational Physics

Background:

• Tin selenide (SnSe) is a promising thermoelectric material with low thermal conductivity.
• Understanding phonon transport in SnSe is crucial but challenging due to complex phase transitions and anharmonicity.

Purpose of the Study:

• To investigate the thermal transport properties of SnSe using advanced computational methods.
• To elucidate the mechanisms behind thermal conductivity anisotropy and temperature dependence in SnSe.

Main Methods:

• Molecular dynamics (MD) simulations were employed.
• A machine-learning interatomic potential, parameterized using the moment tensor potential framework, was developed and validated.
• Equilibrium molecular dynamics simulations were conducted to calculate the thermal conductivity tensor.

Main Results:

• The developed interatomic potential accurately predicted temperature-dependent lattice constants, phonon dispersion, and phase transition temperature.
• Thermal conductivity was calculated for SnSe across a temperature range of 200 K to 900 K.
• The study identified the origins of thermal conductivity anisotropy and the significant role of four-phonon scatterings.

Conclusions:

• The validated machine-learning interatomic potential provides a reliable tool for studying SnSe's thermal and mechanical properties.
• This research offers insights into phonon transport mechanisms, crucial for optimizing SnSe as a thermoelectric material.