基于Mg的高性能热电的机器学习引导设计:热膨胀效应的洞察
Da Wan1, Shulin Bai2, Sirui Fan1
1School of Materials Science and Engineering, Beihang University, Beijing 100191, China; State Key Laboratory of Artificial Intelligence for Material Science, Beihang University, Beijing 100191, China; Tianmushan Laboratory, Hangzhou 311115, China.
热膨胀通过降低热导率和提高Seebeck系数显著增强了基于Mg的热电材料. 机器学习模型加速了这些环保材料的发现.
科学领域:
- 材料科学
- 计算材料科学
- 凝聚物质物理学
背景情况:
- 基于 (Mg) 的材料具有环保性和丰富性,使其成为热电应用的前景.
- 寻找高性能热电材料的传统实验方法耗时且昂贵.
研究的目的:
- 使用高通量计算和机器学习系统评估基于Mg的热电材料.
- 阐明热膨胀在优化热电性能中的作用.
- 开发一个预测模型来加速发现基于Mg的热电材料.
主要方法:
- 用于材料选的高通量计算.
- 机器学习,特别是XGBoost模型,用于属性预测.
- 对材料性能的热膨胀效应的分析.
主要成果:
- 热膨胀对基于Mg的系统的热电功率 (ZT) 有关重要影响.
- 热膨胀增强了材料的和性,降低了晶格的导热性.
- 热膨胀将状态的密度集中在费米水平附近,可能会增加西贝克系数.
结论:
- 热膨胀是优化基于Mg的热电材料的一个关键因素.
- 建立了热电材料优化的通用理论框架.
- 一个精确的XGBoost模型可以快速选和发现基于Mg的高性能热电材料.
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