2022 Fiscal Year Research-status Report
A novel theory of the magnetostriction mechanism using topological data analysis
| Project/Area Number |
22K14590
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| Research Institution | Tokyo University of Science |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Keywords | Magnetostriction / Magnetization / Machine learning / Magnetic domains |
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| Outline of Annual Research Achievements |
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.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
I consider the current status to be slightly delayed. The main reason for this delay was the pre-processing step. Given that the experimental images contains noise and defects, it was necessary to first develop an algorithm capable of removing the noise without losing crucial information. This step impeded the timely progress of the work. However, once the noise was successfully removed, the analysis of the data was able to proceed.
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| Strategy for Future Research Activity |
This year, my focus will be on the optimization of simulations related to ferromagnetic shape memory alloys (FSMAs) and create more data from different parameters. The analysis of the experimental data has highlighted a significant correlation between certain features and both magnetization and magnetostriction. However, using only experimental data, determining the contribution of each energy of the system remains a complex challenge. Therefore, I will prepare a simulation dataset and conduct an in-depth analysis of this data. This analysis will leverage an extended energy model that provides more insight into the physical mechanisms at play.
I am also planning to incorporate topological data analysis to extract the important features of the data as the relation between magnetic domains. Last year I confirm that it is possible to obtain the information of the magnetostriction and magnetization from experimental data, in this year I want to deep in the physics and focus on the mechanism by studying the energy landscape in the unsupervised learning space.
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