Tuning the lattice thermal conductivity of Sb₂Te₃ by Cr doping: a deep potential molecular dynamics study

P Zhang and WK Liao and ZY Zhu and M Qin and ZH Zhang and D Jin and Y Liu and ZY Wang and ZH Lu and R Xiong, PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 25, 15422-15432 (2023).

DOI: 10.1039/d3cp00999h

Element doping is a prominent method for reducing the lattice thermal conductivity and optimizing the thermoelectric performance of materials in the thermoelectric field. However, determination of the thermal conductivity of element-doped systems is a challenging task, especially when the elements are randomly doped. In this work, a first-principles based deep neural network potential (NNP) is developed to investigate the lattice thermal transport properties of Cr-doped Sb2Te3 using molecular dynamics simulations. Compared with pure Sb2Te3, the thermal conductivity of orderly Cr-doped Sb2Te3 with Cr atoms locating at specific atomic layer positions decreases slightly in the in-plane direction, but sharply in the out-of-plane direction. The decrease of the low frequency phonon density of states and the enhancement of phonon scattering near 2.5 THz are the primary reasons for the decrease in the thermal conductivity of Cr-doped Sb2Te3, while the decrease of phonon velocity due to band flattening is the reason for the sharp decrease of thermal conductivity in the out-of-plane direction. Moreover, the thermal conductivities of randomly Cr-doped Sb2Te3 with different Cr concentrations are also investigated using the NNP. It is found that the thermal conductivities in both the in-plane and out-of-plane directions are reduced by 76% and 80%, respectively, for Sb36Cr36Te108. Furthermore, the influence of different Cr dopant arrays on the thermal conductivity of Sb2Te3 is also predicted using the NNP. Our work provides a good example for predicting the thermal conductivity of element-doped systems using the NNP combined with molecular dynamics simulations.

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