基于机器学习的2D GeS和SnS的范德瓦尔斯异构的热导率,基于原子间潜力的机器学习
1School of Arts and Sciences, Shaanxi University of Science and Technology, Xi'an 710021, People's Republic of China.
Journal of physics. Condensed matter : an Institute of Physics journal
|September 5, 2023
概括
在范德瓦尔斯的异构结构中,像GeS和SnS这样的二维材料的垂直堆叠显著抑制了导热性. 这种接口效应为热电应用提供可调节的热传输.
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
- 纳米技术纳米技术
背景情况:
- 范德瓦尔斯的异构结构能够精确调整二维材料的特性.
- 了解分层材料中的热传输对于设备应用至关重要.
研究的目的:
- 研究GeS/SnS范德瓦尔斯异构结构的热传输特性.
- 澄清范德瓦尔斯接口对格子导热性的影响.
主要方法:
- 机器学习的原子间潜力被用来进行系统的调查.
- 分析各种堆叠配置以了解接口效应.
主要成果:
- 范德瓦尔斯接口大大抑制了热传输能力.
- 导热性和板材导电性低于构成单层的材料.
- 增强的接口无声散射导致电导率的抑制和可调的异质性.
结论:
- 范德瓦尔斯接口有效地调节2D化组-IV单化物中的热传输.
- 在SnS/GeS异构结构中,压抑的晶格导热性和接口依赖的声子传输显示了热电应用的潜力.
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