Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning
Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning
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DOI：
10.1038/s41524-020-00485-2
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发表时间：
2021-01-29
影响因子：
9.7
通讯作者：
Nakatani, Yoshinobu
中科院分区：
文献类型：
--
作者：
Kawaguchi, Masashi;Tanabe, Kenji;Nakatani, Yoshinobu
机器学习应用于大量现代设备，这些设备对于建设节能智能社会至关重要。音频和人脸识别是利用这种人工智能的最著名的技术。在材料研究中，机器学习适用于预测具有某些功能的材料，这种方法通常被称为材料信息学。在这里，我们展示了机器学习可以用来从实验中获得的单个图像中提取材料参数。Dzyaloshinskii-Moriya （DM）相互作用和薄膜异质结构的磁各向异性分布是开发下一代存储级磁存储技术的关键参数。微磁仿真用于生成数千张随机图像，用于训练和模型验证。采用卷积神经网络系统作为学习工具。利用训练系统对典型co基薄膜异质结构的DM交换常数进行了研究，其估价值与实验值吻合较好。此外，我们还证明了该系统可以独立地确定磁各向异性分布，展示了模式识别的潜力。这种方法可以大大简化实验过程，拓宽材料研究的范围。
Machine learning is applied to a large number of modern devices that are essential in building an energy-efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial intelligence. In materials research, machine learning is adapted to predict materials with certain functionalities, an approach often referred to as materials informatics. Here, we show that machine learning can be used to extract material parameters from a single image obtained in experiments. The Dzyaloshinskii-Moriya (DM) interaction and the magnetic anisotropy distribution of thin-film heterostructures, parameters that are critical in developing next-generation storage class magnetic memory technologies, are estimated from a magnetic domain image. Micromagnetic simulation is used to generate thousands of random images for training and model validation. A convolutional neural network system is employed as the learning tool. The DM exchange constant of typical Co-based thin-film heterostructures is studied using the trained system: the estimated values are in good agreement with experiments. Moreover, we show that the system can independently determine the magnetic anisotropy distribution, demonstrating the potential of pattern recognition. This approach can considerably simplify experimental processes and broaden the scope of materials research.