用机器学习从磁泡图像快速准确估算界面自旋轨道耦合强度
Data-Driven Estimation of the interfacial Dzyaloshinskii-Moriya Interaction with Machine Learning
- 构建卷积神经网络，直接从模拟的磁泡图像中推断DMI强度
- 在噪声、不均匀性和低分辨率下仍保持高精度预测能力
- 适合需要快速定量分析磁性材料界面相互作用的研究者
机器学习为实验技术提供了强大支持，尤其适用于从大数据中提取隐含特征。在磁性材料中，界面Dzyaloshinskii-Moriya相互作用（DMI）强度的精确估计仍具挑战性，因现有实验方法多依赖间接测量，且不同技术结果常不一致。由于该相互作用通常从磁泡畴扩展中提取，我们探究仅凭磁泡纹理是否足以实现数据驱动的DMI推断。为此，我们开发了一个紧凑的卷积神经网络，训练于涵盖结构非均匀性、加性噪声和图像像素化的完整微磁学数据集，该数据集模拟了磁光克尔效应成像。所提网络对样品不均匀性、噪声及降低的空间分辨率表现出强鲁棒性，并在训练区间外仍能可靠泛化，准确预测DMI值。这些结果表明，机器学习可作为快速、定量表征具有界面DMI的磁性纹理的有效工具。
原文摘要 · Abstract (English)
Machine learning offers powerful tools to support experimental techniques, particularly for extracting latent features from large datasets. In magnetic materials, accurately estimating the interfacial Dzyaloshinskii-Moriya interaction strength remains challenging, as existing experimental methods often rely on indirect measurements and can yield inconsistent results across techniques. Because this interaction is often extracted experimentally from bubble domain expansion, we investigate whether bubble textures alone contain sufficient and reliable information for data driven DMI inference. We therefore develop a compact convolutional neural network trained on a comprehensive micromagnetic dataset of magnetic bubble domains designed to emulate magneto optical Kerr effect imaging, including structural non uniformity, additive noise, and image pixelation. The proposed network demonstrates strong robustness against sample inhomogeneities, noise, and reduced spatial resolution. Furthermore, it exhibits reliable generalization by accurately predicting DMI values outside the trained interval. These results support the use of machine learning as a fast and quantitative tool to characterize magnetic textures with interfacial DMI.
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