The explosive growth of electric vehicles has intensified the search for ways to increase the energy efficiency of electric motors. One of the big challenges is iron loss, also known as magnetic hysteresis loss. Iron losses occur when the magnetic field inside the motor repeatedly reverses direction. This process wastes energy as heat inside the motor core, which is made of soft magnetic material. Since electric motors often operate at high temperatures, thermal effects can also partially demagnetize these materials, further complicating the energy loss issue.
A key factor behind these effects is the behavior of magnetic domains, which are small magnetic regions within the material. The arrangement and structure of these domains strongly influences how magnetic materials respond to heat and how much energy is lost during operation.
complex magnetic maze domain
Some soft magnetic materials contain highly complex magnetic structures called maze domains, named for their zigzag, maze-like appearance. These labyrinth regions can change abruptly as the temperature increases or decreases, which can affect the loss of energy within the material. However, scientists have struggled to fully understand these structures because many interacting factors are involved, including material microstructure, thermal effects, and energy stability.
To better understand this behavior, researchers led by Professor Masato Koji and Dr. Takeshi Masuzawa from the Department of Materials Science and Engineering at Tokyo University of Science (TUS), in collaboration with collaborators from the University of Tsukuba, Okayama University, and Kyoto University, developed a new model called the Entropy Extended Ginzburg-Landau (eX-GL) model. The research team used this approach to study the energy landscape of labyrinthine domains in rare earth iron garnet (RIG).
“Traditional simulations oversimplify the actual materials, while experiments reveal complexity because there is no clear way to quantify cause and effect,” Professor Koji explains. “Our physics-based explainable artificial intelligence framework is designed to address these limitations and mechanistically explain the temperature-dependent magnetization reversal process.”
Their findings were published in the scientific journal Scientific Reports.
AI and physics reveal hidden magnetic behavior
To investigate how temperature affects the demagnetization of the labyrinth region, the researchers took microscopic images of magnetic domains in RIG samples at different temperatures. These images were analyzed using the eX-GL model.
The first stage of the model uses persistent homology (PH), an advanced mathematical method that identifies topological features in the data. This enabled the research team to detect non-uniform structural characteristics in magnetic domain images. They then used machine learning-based pattern recognition to identify the most important features from the PH data, producing a digital free energy landscape that tracks how the magnetic microstructure evolves in response to changes in energy. Finally, mathematical analysis correlated these microscopic domain structures with larger magnetization reversal processes.
Using this method, the researchers were able to identify a dominant feature known as PC1 and capture the magnetization reversal process. By linking PC1 with physical properties, the research team visualized four major energy barriers that strongly influence magnetization reversal dynamics.
Hidden energy barriers within magnetic materials
Detailed analysis of these barriers and associated microstructures reveals how different forms of energy influence magnetization reversal. The researchers measured energy transfer, including exchange interactions, demagnetization effects, and entropy.
They also found that maze domains become more complex as the length of the domain walls increases. This increase in complexity is caused by the interaction between entropy and exchange forces. These results helped to elucidate the physical mechanism behind the maze area reversal behavior.
“Our eX-GL approach effectively automates the interpretation of complex magnetization reversal processes and enables the identification of hidden mechanisms that are difficult to identify using traditional methods,” says Professor Koji. “Furthermore, since free energy is a universal thermodynamic metric, our model can be extended to other systems with similar properties.”
Overall, this study not only sheds light on the workings of labyrinthine regions, but also introduces broader strategies for investigating complex energy landscapes in magnetic systems and other related physical materials.
This research was supported by the Japan Society for the Promotion of Science (KAKENHI) Grant-in-Aid for Scientific Research (A) (21H04656). Additional support was provided by JST-CREST (grant number JPMJCR21O1). C. Mitsumata was supported by the Research Center for Energy and Materials Science (TREMS), University of Tsukuba.
/Open to the public. This material from the original organization/author may be of a contemporary nature and has been edited for clarity, style, and length. Mirage.News does not take any institutional position or position, and all views, positions, and conclusions expressed herein are those of the authors alone. Read the full text here.
