对具有化学结合特征的潜在铁基半Heusler热电材料的数据驱动方法
Xue Nan1, Kei Hayashi1, Zhicheng Huang1
1Department of Applied Physics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.
Science advances
|June 27, 2025
概括
探索半海斯勒化合物中的化学结合揭示了强大的Y-Z结合与优越的机械和热电性质相关. -铁-表现出卓越的性能,加速了先进的热电材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 固态物理 固态物理
- 计算化学的计算化学
背景情况:
- 化学结合显著影响材料特性,指导高性能材料的发现.
- 半豪斯勒 (HH) 化合物表现出复杂的结合,具有共价和离子特征,影响机械和热电行为.
研究的目的:
- 用数据驱动的方法识别有前途的HH化合物.
- 探索HH化合物中的粘合特征和功能性质之间的相关性.
- 为了加快对潜在的热电材料的选.
主要方法:
- 在数据驱动分析中使用了第一原则计算.
- 电子结构和轨道解析的结合分析被用来解释化学结合.
- 研究了结构与财产的相关性.
主要成果:
- 增强的Y-Z结合与增加的散装模量 (B),更大的格鲁尼森参数和改进的功率因子相关.
- 铁表现出极好的散体模量 (162.4 GPa) 和低晶格导热率 (~7.7 W/m·K).
- -铁-在526K达到~0.52的功效值,在没有纳米结构的情况下,-铁-的性能超过了~80.8%.
结论:
- 化学结合特征为结构与属性关系提供了深入的见解.
- 了解粘合对于加速发现高性能热电材料至关重要.
- 数据驱动的方法有效地选HH化合物,以获得卓越的功能性质.
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