Accessing the thermal conductivities of Sb₂Te₃ and Bi₂Te₃/Sb₂Te₃ superlattices by molecular dynamics simulations with a deep neural network potential

P Zhang and M Qin and ZH Zhang and D Jin and Y Liu and ZY Wang and ZH Lu and J Shi and R Xiong, PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 25, 6164-6174 (2023).

DOI: 10.1039/d2cp05590b

Phonon thermal transport is a key feature for the operation of thermoelectric materials, but it is challenging to accurately calculate the thermal conductivity of materials with strong anharmonicity or large cells. In this work, a deep neural network potential (NNP) is developed using a dataset based on density functional theory (DFT) and applied to describe the lattice dynamics of Sb2Te3 and Bi2Te3/Sb2Te3 superlattices. The lattice thermal conductivities of Sb2Te3 are first predicted using equilibrium molecular dynamics (EMD) simulations combined with an NNP and the results match well with experimental values. Then, through further exploration of weighted phase spaces and the Gruneisen parameter, we find that there is a stronger anharmonicity in the out-of- plane direction in Sb2Te3, which is the reason why the thermal conductivities are overestimated more in the out-of-plane direction than in the in-plane direction by solving the phonon Boltzmann transport equation (BTE) with only three-phonon scattering processes being considered. More importantly, the lattice thermal conductivities of Bi2Te3/Sb2Te3 superlattices with different periods are accurately predicted using non-equilibrium molecular dynamics (NEMD) simulations together with an NNP, which serves as a good example to explore the thermal transport physics of superlattices using a deep neural network potential.

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