通过Cr兴奋剂调节Sb2Te3的晶格导热率:一个深度潜在的分子动力学研究
Pan Zhang1, Wenkai Liao1, Ziyang Zhu1
1Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China. xiongrui@whu.edu.cn.
Physical chemistry chemical physics : PCCP
|May 30, 2023
概括
元素兴奋剂通过降低导热率来优化热电材料. 一个新的深度神经网络潜力准确地预测了Cr-doped Sb2Te3中的热传输,显示了随机doped系统的导电率显著降低.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 计算材料科学科学 计算材料科学
背景情况:
- 在调整热电材料性能方面,元素兴奋剂至关重要.
- 准确预测多系统中的导热率,特别是随机多,仍然是一个挑战.
研究的目的:
- 开发基于第一原则的深度神经网络潜力 (NNP) 来研究格子热传输.
- 通过分子动力学 (MD) 模拟来研究Cr-doped Sb2Te3的热电性能.
主要方法:
- 开发了基于第一原则计算的深度神经网络潜力 (NNP).
- 采用分子动力学 (MD) 模拟来研究导热性.
- 分析了状态的声子密度,声子散射和声子速度.
主要成果:
- 在Sb2Te3中Cr注显著降低了格子导热率,特别是在外平面方向.
- 随机Cr-doped的Sb2Te3显示在平面内降低了76%的热导率,在平面外降低了80%.
- 确定了声子密度下降的状态和增强的声子散射作为热导率降低的关键因素.
结论:
- 与MD模拟相结合的NNP对于预测元素杂系统中的导热率是有效的.
- 碳化为优化Sb2Te3.3中的热电性能提供了一种可行的策略.
- 了解剂排列对于精确的导热控制至关重要.
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