用卷积神经网络和偏差场受约束的微磁放松重建磁结构和材料参数
Dieter Suess1, Alexander Setescak2, Jack Smith2
1Physics of Functional Materials, University of Vienna, Kolingasse 14-16, 1090, Vienna, Austria. dieter.suess@univie.ac.at.
Scientific reports
|December 1, 2025
概括
本研究介绍了一种框架,可以使用兰道-利夫希茨-吉尔伯特动力学从流浪场测量中提取磁性材料参数. 该方法准确地恢复参数并重建磁化纹理,即使有噪音数据.
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
- 计算物理 计算物理
背景情况:
- 对磁性材料的准确表征对于开发先进的自旋电子设备至关重要.
- 从实验数据中提取内在磁性参数和内部结构仍然具有挑战性.
研究的目的:
- 开发一个计算框架,从流浪场测量中提取磁性材料参数和内部结构.
- 提高微磁模拟和材料表征的准确性和稳定性.
主要方法:
- 使用Landau-Lifshitz-Gilbert动力学与可调整的偏差场.
- 尽量减少模拟和实验迷路场或MFM频率转移对比之间的不匹配.
- 采用卷积神经网络 (U-Net) 来进行磁化纹理重建.
主要成果:
- 成功地恢复了全球磁性参数,包括单轴无极性,和磁化,交换刚性和Dzyaloshinskii-Moriya相互作用.
- 在流浪现场数据中证明了适度噪音水平的稳定性.
- 实现了磁化纹理的改进重建,特别是在具有挑战性的弱迷路场区域中,使用基于混合物理和深度学习的混合物理方法.
结论:
- 开发的框架为磁性材料表征提供了一种灵活而强大的方法.
- 基于混合物理的机器学习方法提高了微磁模拟和反向问题解决的准确性.
- PyTorch实现方便了基于梯度的优化和未来的扩展,用于空间解析的参数重建.
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