机器学习delta-T噪声用于温度偏差估计
Matthew Gerry1, Jonathan J Wang2, Joanna Li1,3
1Department of Physics, University of Toronto, 60 Saint George St., Toronto, Ontario M5S 1A7, Canada.
The Journal of chemical physics
|February 26, 2025
概括
机器学习使用delta-T噪声估计原子尺度电子连接处的温度偏差. 通过多个结点的平均测量,可以提高1克尔文的精度,从而能够精确控制热刺激.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 量子电子学 量子电子学
- 纳米技术纳米技术
背景情况:
- 德尔塔-T射击噪声在原子尺度上的温度偏差电子连接中产生.
- 它表现出对温度差异和非线性传导系数的二次依赖.
研究的目的:
- 为了证明使用delta-T噪声来估计原子尺度连接处的温度偏差.
- 开发一种用于预测温度差异的机器学习方法.
主要方法:
- 在合成和实验数据上训练的神经网络的监督机器学习.
- 输入特征包括缩放电导率,delta-T噪声和平均温度.
- 在原子连接处对delta-T噪声测量的集成平均值.
主要成果:
- 在合成数据上训练的一个神经网络准确地从实验数据中预测了温度偏差.
- 在高达4G0电导度的连接处,平均偏差小于1凯尔文.
- 整体平均提高了预测准确性,克服了单个测量的局限性.
结论:
- 当对一组连接点进行平均值时,Delta-T噪声可以准确估计温度偏差.
- 机器学习提供了一种可行的方法来估计电子连接处的温度偏差和其他刺激.
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