Unphysical grain size dependence of lattice thermal conductivity in Mg3(Sb, Bi)2: An atomistic view of concentration dependent segregation effects

Xiaofan Huang, Chengzhi Li,Minhui Yuan,Jing Shuai,Xiang-Guo Li, Yanglong Hou

Materials Today Physics(2024)

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摘要
Grain boundary (GB) engineering is one of the most common strategies to reduce the lattice thermal conductivity and improve thermoelectric materials. However, in the case of the promising thermoelectric material Mg3(Sb1−xBix)2, an abnormal dependence of lattice thermal conductivity on grain size was observed at the concentration of x = 0.25, where smaller grain polycrystalline materials exhibited higher lattice thermal conductivity compared to larger grain materials. The proposed theory about the overestimation of lattice thermal conductivity in inhomogeneous materials with GBs cannot clarify the concentration dependence of this anomaly. Here we elucidate the interplay between segregation, concentration, and lattice thermal conductivity in Mg3(Sb1−xBix)2 alloys through atomistic simulations with a highly accurate machine learning interatomic potential. We find the largest segregation of Bi atoms to GBs in Mg3(Sb1−xBix)2 at the concentration of x = 0.25 for both twist and tilt GB structures due to the combination effects of site spectrality and solute interactions. Our molecular dynamic simulations demonstrate that the pronounced segregation of heavier Bi atoms, particularly at x = 0.25, leads to a substantial increase in the lattice thermal resistance at GBs, thus contributing to the degree of inhomogeneity. The concentration dependent segregation reveals the atomic origin of the observed unphysical inverse relationship between grain size and lattice thermal conductivity at the specific concentration of x = 0.25. These results highlight the need to design the alloy concentration to tune the atomic segregation and tailor the thermal properties of thermoelectric materials with GBs.
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关键词
Lattice thermal conductivity,Zintl alloy thermoelectric,Concentration effects,Machine learning interatomic potential,Atomistic simulation
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