Japanese researchers developed an analysis method, based on persistent homology and principal component analysis, to quantify complex changes in the microscopic magnetic domain structures of magnetic materials. Image: Kotsugi Laboratory from Tokyo University of Science, Japan.The reliability of data storage and writing speed in advanced magnetic devices depends on drastic, complex changes in microscopic magnetic domain structures. Quantifying these changes has, however, proved extremely challenging, limiting scientists’ understanding of magnetic phenomena. To tackle this, researchers from Japan used machine learning and topology to develop an analysis method that can quantify the complexity of magnetic domain structures, revealing hidden features of magnetization reversal.
Spintronic devices and their operation are governed by the microstructures of magnetic domains. These magnetic domain structures undergo complex, drastic changes, including reversal, when an external magnetic field is applied to them. Unfortunately, the resulting fine structures are not reproducible, and it is challenging to quantify the complexity of magnetic domain structures.
Scientists’ understanding of the magnetization reversal phenomenon is thus limited to crude visual inspections and qualitative methods, representing a severe bottleneck in material design. Even predicting the stability and shape of the magnetic domain structures in Permalloy, a well-known material studied for over a century, has proved difficult.
Addressing this issue, a team of researchers headed by Masato Kotsugi from Tokyo University of Science recently developed an artificial-intelligence-based method for analyzing material functions in a more quantitative manner. In a paper in the Science and Technology of Advanced Materials: Methods, the team reports using topological data analysis to develop a super-hierarchical and explanatory analysis method for magnetic reversal processes.
According to the research team, the term super-hierarchical refers to the connection between micro and macro properties, which are usually treated as isolated but actually contribute jointly to a physical explanation.
The team quantified the complexity of the magnetic domain structures using persistent homology, a mathematical tool from computational topology that measures topological features of data persisting across multiple scales. The team further visualized the magnetization reversal process in two-dimensional space using principal component analysis, a data analysis procedure that summarizes large datasets using smaller ‘summary indices’, facilitating better visualization and analysis.
“The topological data analysis can be used for explaining the complex magnetization reversal process and evaluating the stability of the magnetic domain structure quantitatively,” said Kotsugi.
This analysis revealed that slight changes in the magnetic domain structure, invisible to the human eye, indicated a hidden feature dominating the metastable/stable reversal processes. The team also successfully determined the cause of the branching of the macroscopic reversal process in the original microscopic magnetic domain structure.
The novelty of this research lies in its ability to connect magnetic domain microstructures and macroscopic magnetic functions freely across hierarchies by applying the latest mathematical advances in topology and machine learning. This allows the detection of subtle microscopic changes and subsequent prediction of stable/metastable states.
“This super-hierarchical and explanatory analysis would improve the reliability of spintronics devices and our understanding of stochastic/deterministic magnetization reversal phenomena,” says Kotsugi.
Interestingly, the new algorithm, with its superior explanatory capability, can also be applied to chaotic phenomenon like the butterfly effect. On the technological front, it could potentially improve the reliability of next-generation magnetic memory writing and aid the development of new hardware for the next generation of devices.
This story is adapted from material from Tokyo University of Science, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.