Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry
Machine-learning recognition of Dzyaloshinskii-Moriya interaction from magnetometry
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DOI：
10.1103/physrevresearch.5.043012
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发表时间：
2023-04
影响因子：
4.2
通讯作者：
Bradley J. Fugetta;Zhijie Chen;D. Bhattacharya;Kun Yue;Kai Liu;A. Liu;G. Yin
中科院分区：
文献类型：
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作者：
Bradley J. Fugetta;Zhijie Chen;D. Bhattacharya;Kun Yue;Kai Liu;A. Liu;G. Yin
Dzyaloshinskii-Moriya 相互作用 (DMI) 是相邻局部自旋之间交换相互作用的反对称部分，它缠绕自旋流形并可以稳定非平凡的拓扑自旋纹理。由于拓扑是一种强大的信息载体，因此可以提取 DMI 幅度的表征技术对于自旋电子材料的发现和优化非常重要。现有的 DMI 定量测定实验技术，例如自旋纹理的高分辨率磁成像以及磁振子或传输特性的测量，非常耗时且需要专门的仪器。在这里，我们展示了卷积神经网络可以从较小的磁滞回线或材料的磁性“指纹”中提取 DMI 幅度。这些磁滞回线可以通过传统的磁力测量轻松获得。这为研究下一代信息处理的拓扑自旋纹理提供了一个方便的工具。
The Dzyaloshinskii-Moriya interaction (DMI), which is the antisymmetric part of the exchange interaction between neighboring local spins, winds the spin manifold and can stabilize non-trivial topological spin textures. Since topology is a robust information carrier, characterization techniques that can extract the DMI magnitude are important for the discovery and optimization of spintronic materials. Existing experimental techniques for quantitative determination of DMI, such as high-resolution magnetic imaging of spin textures and measurement of magnon or transport properties, are time consuming and require specialized instrumentation. Here we show that a convolutional neural network can extract the DMI magnitude from minor hysteresis loops, or magnetic"fingerprints"of a material. These hysteresis loops are readily available by conventional magnetometry measurements. This provides a convenient tool to investigate topological spin textures for next-generation information processing.