Making Sense of Coercivity in Magnetic Materials with Machine Learning

Making Sense of Coercivity in Magnetic Materials with Machine Learning

Soft magnetic materials, that is, materials that can be easily magnetized and demagnetized, play a vital role in transformers, generators, and motors. The ability of a magnetic material to resist an external magnetic field without changing its magnetization is known as “coercivity”, a property closely related to energy loss. In applications such as electric cars, low coercivity materials are highly desirable to achieve higher energy efficiency.

However, coercivity and other magnetic phenomena associated with energy losses in soft magnetic materials arise from very complex interactions. The usual large-scale analysis suffers from oversimplification of the material structure and often requires additional parameters to fit theory to experiment. So far, although coercivity analysis tools and frameworks are widely available, they generally do not directly consider material defects and boundaries, which is fundamental for developing new applications.

In this context, a research team including Professor Masato Kotsugi from Tokyo University of Science (TUS), Japan, has recently developed a novel approach to relate microscale features to a macroscopic physical property, the coercivity, using a combination of data science, machine learning, and an extension of the GL model. This studyled by Dr Alexandre Lira Foggiatto of TUS, was published in Physics of communications.

The team aimed to find a way to automate the coercivity analysis of magnetic materials while taking into account their microstructural characteristics. To this end, they first collected data for simulated and real magnetic materials in the form of microscopic images of their magnetic domains. The images, after pre-processing, were used as input data for a machine learning technique called principal component analysis (PCA), which is commonly used to analyze large data sets. Using PCA, the team condensed the most relevant information (features) from these pre-processed images into a two-dimensional “feature space”.

This approach, combined with other machine learning techniques, such as artificial neural networks, allowed the researchers to visualize a realistic energy landscape of magnetization reversal in the material in feature space. A careful comparison of experimental and simulated image results demonstrated that the proposed methodology was a practical strategy to map the material’s most important features in a meaningful way. “Describing the energy landscape using machine learning has shown good results for both experimental and simulated data. Both shared similar shapes as well as similar explanatory variables and correlations between them,” Foggiatto remarks.

Overall, this study shows how materials informatics can be intelligently harnessed not only to automate but also to clarify the physical origin of coercivity in soft magnetic materials. Hopefully, this will help materials scientists and physicists derive new physical laws and models to go beyond state-of-the-art models and frameworks. Moreover, the applications of this strategy go well beyond coercivity, as Foggiatto points out: “Our method can be extended to other systems to analyze properties such as temperature and strain/strain, as well as the dynamics of high-speed magnetization inversion processes. »

Interestingly, this is the second published study by Kotsugi and co-workers of the extended Landau free energy model they are developing. They hope that in the near future, their functional analysis models will help achieve high efficiency in electric car motors, paving the way for more sustainable transportation.

– This press release was originally posted on the Tokyo University of Science website

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