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Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

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San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...
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Temperature Dependent Deformation01:12

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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...
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使用层级高斯过程回归的高温电阻聚合物数据驱动的设计.

Jiale Zhang1,2,3, Aocheng Fan1,2,3, Ziqi Wang1,2,3

  • 1School of Materials Science and Engineering, Beihang University, Beijing 100191, China.

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概括

预测聚胺的玻璃化过渡温度 (Tg) 对高温应用至关重要. 一种新的机器学习方法使用有限的数据准确预测Tg,有助于设计先进材料.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 聚合物科学 聚合物科学

背景情况:

  • 对聚胺玻璃过渡温度 (Tg) 的准确预测对于航空航天,电子和显示技术至关重要.
  • 由于合成,仪器和表征的局限性,实验性Tg确定具有挑战性.
  • 分子动力学模拟在Tg预测的准确性和验证方面存在局限性.

研究的目的:

  • 开发一种机器学习 (ML) 方法,用于使用小样本数据集准确地预测聚胺的Tg.
  • 将先前的知识整合到一个层次化的高斯过程回归模型中.
  • 确定影响聚胺Tg的关键分子描述因素.

主要方法:

  • 利用RDKit进行分子描述器计算和特征选择,识别了21个关键描述器.
  • 采用了分层高斯过程回归ML方法,整合了先前的知识.
  • 应用了Shapley添加式解释 (SHAP) 来进行特征重要性分析和贝叶斯更新策略来改进模型.

主要成果:

  • 实现了特殊的模型性能,R2值为0.98 (训练) 和0.74 (测试),表现优于传统的ML方法.
  • 确定可旋转债券的数量和最低部分负债作为影响Tg的主要因素.
  • 实验和模拟验证显示预测误差低于15%,并对高Tg方案进行了校正.

结论:

  • 开发了一个强大且经过验证的ML工具,用于预测聚胺Tg,解决数据稀缺问题.
  • 阐明了用于设计热稳定聚胺的关键结构-属性关系.
  • 建立了数据驱动材料设计的可转移框架,加速了高性能聚合物开发.