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相关概念视频

Temperature Dependent Deformation01:12

Temperature Dependent Deformation

137
In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
137

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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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在有限温度下与机器学习力场的点缺陷形成.

Irea Mosquera-Lois1, Johan Klarbring1,2, Aron Walsh1

  • 1Thomas Young Centre & Department of Materials, Imperial College London London SW7 2AZ UK a.walsh@imperial.ac.uk.

Chemical science
|April 24, 2025
PubMed
概括

有限温度效应通过使动态缺陷行为显著影响材料性能. 机器学习力场揭示了热振动和配置可以改变缺陷度,影响材料性能.

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算材料科学科学 计算材料科学
  • 固态物理 固态物理

背景情况:

  • 点缺陷是功能性材料性质的关键决定因素.
  • 传统的缺陷热力学建模使用静态近似,忽视热效应.
  • 静态模型虽然在计算上便宜,但在有限的温度下忽略了振动和配置贡献.

研究的目的:

  • 使用机器学习力场 (MLFFs) 调查动态缺陷行为.
  • 量化各种贡献对无缺陷能量的影响.
  • 为了比较不同的计算方法来计算无缺陷能量.

主要方法:

  • 训练了一种机器学习力场 (MLFF) 用于动态缺陷建模.
  • 在 Telluride (CdTe) 中研究了间位 (Te+1i) 和空位 (V+2Te).
  • 对比和和非和方法,包括热力学集成,用于自由能量计算.

主要成果:

  • 超稳定缺陷配置在室温下被填充.
  • 发现热效应将Te+1i缺陷的预测度增加了两个数量级.
  • 有限温度效应显著影响预测的材料特性.

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结论:

  • 有限温度效应对于准确建模缺陷热力学至关重要.
  • 机器学习力场 (MLFFs) 提供了一种强大的方法来探索缺陷动态.
  • MLFF适用于在合成和操作温度下研究缺陷行为.