高温磁电阻的先进机器学习模型对Ni81Fe19单层的预测
Tarik Akan1, Perihan Aksu2,3, Recep Sahingoz1
1Physics Department, Yozgat Bozok University, 66100 Yozgat, Türkiye.
Nanomaterials (Basel, Switzerland)
|January 9, 2026
概括
这项研究使用机器学习来预测超出实验温度的Ni81Fe19薄膜的磁阻. 这些模型准确地预测材料特性,并估计基里温度 (TC).
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
- 机器学习应用 机器学习应用
背景情况:
- 多晶Ni81Fe19薄膜对于磁传感器应用至关重要.
- 在广泛的温度范围内了解磁阻 (MR) 行为对于设备性能至关重要.
- 实验的局限性往往限制了在极端温度下直接测量材料特性.
研究的目的:
- 为了研究Ni81Fe19薄膜的磁电阻.
- 采用机器学习来预测超出实验限制的MR特性.
- 为了准确估计Ni81Fe19.1的基里温度 (TC).
主要方法:
- 5纳米Ni81Fe19薄膜在Si晶片上的喷雾沉积.
- 使用范德波夫技术从25°C到350°C的磁阻测量.
- 随机森林回归的应用用于预测建模和库里温度估计.
主要成果:
- 磁电阻被分析为温度和磁场的函数.
- 机器学习模型实现了高准确度 (R2高达0.9449) 的预测.
- 库里温度估计大约为590.97°C.
结论:
- 机器学习可以有效地预测磁电阻行为和临界温度过渡.
- 机器学习模型提供了一个强大的工具,用于预测超越实验界限的材料特性.
- 这种方法提高了对Ni81Fe19.19等磁性材料的理解和设计.
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