A novel theory of the magnetostriction mechanism using topological data analysis

使用拓扑数据分析的磁致伸缩机制的新理论

• 批准号: 22K14590
• 负责人: LIRAFOGGIATTO ALEXANDRE
• 金额: $ 3万
• 依托单位: Tokyo University of Science
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K14590/
• 关键词: Magnetostriction; Magnetization; Machine learning; Magnetic domains; Persistent homology; Machine Learning

Throughout last year, I have obtained a Kerr microscope image dataset of single crystal Fe-Ga alloy, from others collaborators. Working in cooperation with other colleagues, the data has been pre-processed to remove noise and scratches on the surface of the sample in the image data. The pre-processing has been done by a combination of robust principal component analysis (PCA) and singular value decomposition. For the analysis, I have been able to use unsupervised machine learning to separate the contributions of magnetization and magnetostriction based solely on the image data. I used a combination of PCA and fast Fourier transformation to extract the main features of the image data and connected to the physical parameters. I have observed that PCA could effectively distinguish the movements and types of domain walls, specifically 90 and 180-degree domains. By observing the PCA decomposition, I could observe that the first component (PC1) has a directly correlation with the 180-degree domain walls, while the second component could obtain the information from the 180 and 90-degree domain wall. In conclusion, I could observe and assign physical meaning to PCA features for experimental image data. Also, this results shows that multi-physics can be analyzed by the developing method.

在去年，我从其他合作者那里获得了单晶Fe-GA合金的Kerr显微镜图像数据集。 与其他同事合作，已对数据进行了预处理，以消除图像数据中样本表面上的噪声和划痕。预处理是通过强大的主成分分析（PCA）和奇异值分解的结合来完成的。为了进行分析，我已经能够使用无监督的机器学习来仅根据图像数据分离磁化和磁截图的贡献。我使用PCA和快速傅立叶变换的组合来提取图像数据的主要特征并连接到物理参数。我观察到PCA可以有效区分域壁的运动和类型，特别是90度和180度域。通过观察PCA分解，我可以观察到第一个组件（PC1）与180度域壁直接相关，而第二个组件可以从180和90度域壁中获取信息。总之，我可以观察并为PCA特征分配实验图像数据的物理含义。此外，这结果表明，可以通过开发方法对多物理学进行分析。

项目成果

Interpretation of Coercivity and Energy Mechanism based on the ex-GL model

基于ex-GL模型的矫顽力和能量机制解读

• 发表时间: 2023
• 作者: 葉山 智絢;門脇 万里子;片山 英樹;渡辺日香里;四反田功;板垣昌幸;Alexandre Lira Foggiatto
• 通讯作者: Alexandre Lira Foggiatto