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Updated: Jun 12, 2026

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Characterization of Thermal Transport in One-dimensional Solid Materials
Published on: January 26, 2014
重温多体相互作用热电流和热导电计算,使用时刻电位电位/LAMMPS接口
Siu Ting Tai1, Chen Wang2, Ruihuan Cheng1
1Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
Journal of chemical theory and computation
|March 29, 2025
概括
机器学习潜力 (MLP) 需要准确的多体相互作用来计算晶格导热率. 这项研究将多体热电流描述集成到瞬间张量潜力中,显著影响热导率结果.
科学领域:
- 计算材料科学科学 计算材料科学
- 凝聚物质物理学 凝聚物质物理学
- 机器学习在科学中的应用
背景情况:
- 对于具有非对式添加相互作用的系统,热电流运行者的定义对于机器学习潜力 (MLP) 模型是模糊的.
- 现有的实证潜能模型和当前的MLP实现在准确计算格子导热率 (κ) 方面存在挑战.
- 在分子动力学 (MD) 模拟中,在模拟恒温器和对式计算器之间观察到节能不一致.
研究的目的:
- 为了研究多体相互作用在热电流计算中的意义,在当下张力电位 (MTP) 框架内.
- 用MLP解决围绕格子导热率计算的模两可和争议.
- 开发和实施对MLP进行改进的热流描述.
主要方法:
- 在四种不同的材料上进行了不平衡和平衡分子动力学 (MD) 模拟.
- 整合了一个新的病毒应力张量表达式,将多体热电流描述纳入MTP.
- 用修改的MTP获得的模拟结果与标准方法进行比较.
主要成果:
- 在模拟恒温器和对式计算器之间,在节能方面发现了不一致.
- 证明了MTP中新的多体热流描述可以在平衡MD模拟中将晶格导热率 (κ) 结果改变29-64%.
- 突出了纳入多体效应对热传输预测的重大影响.
结论:
- 在使用MLP的MD模拟中,多体描述对于准确的热分析至关重要.
- 在MTP中开发的多体热流配方为计算格子导热率提供了更可靠的方法.
- 这项工作强调了在计算热传输研究中仔细考虑相互作用潜力的必要性.
相关概念视频
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