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.

Wenkai Liao

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