钙钛矿(结构)
空位缺陷
热导率
材料科学
电导率
分子动力学
化学物理
热的
凝聚态物理
纳米技术
热力学
物理化学
化学
计算化学
结晶学
复合材料
物理
作者
Song Han,Yujin Ji,Youyong Li
出处
期刊:Nanoscale
[Royal Society of Chemistry]
日期:2025-01-01
卷期号:17 (11): 6793-6803
被引量:3
摘要
In addition to its excellent photoelectronic properties, the CsPbBr3 perovskite has been reported as a low thermal conductivity (k) material. However, few studies investigated the microscopic mechanisms underlying its low k. Studying its thermal transport behavior is crucial for understanding its thermal properties and thus improving its thermal stability. Here, we train a DFT-level deep-learning potential (DP) of CsPbBr3 and explore its ultra-low k using nonequilibrium molecular dynamics (NEMD). The k calculated using NEMD is 0.43 ± 0.01 W m-1 K-1, which is consistent with experimental results. Furthermore, the Cs vacancy contributes to the decrease in k due to the distortion of the Pb-Br cage, which enhances phonon scattering and reduces the phonon lifetime. Our research reveals the significant potential of machine learning force fields in thermal and phonon behavior research and the valuable insights gained from defect-regulated thermal conductivity.
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