Explainable AI-based physical theory for designing advanced materials

Explainable AI-based physical theory for designing advanced materials

Extension of Landau's free energy model.

image: An image illustrating the extended landau free-energy model developed by a research team at Tokyo University of Science, which allows causal analysis of magnetization reversal in nanomagnets. Using this model, the team was able to effectively visualize the images of the magnetic domain and succeeded in the reverse design of low-power nanostructures.
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Credit: Tokyo University of Science Kotsugi Laboratory, Japan.

Microscopic analysis of materials is essential to achieve desirable performance in next-generation nanoelectronic devices, such as low power consumption and high speeds. However, the magnetic materials involved in such devices often exhibit incredibly complex interactions between nanostructures and magnetic domains. This, in turn, makes functional design difficult.

Traditionally, researchers have performed visual analysis of microscopic image data. However, this often makes the interpretation of this data qualitative and highly subjective. What is missing is a causal analysis of the mechanisms underlying complex interactions in nanoscale magnetic materials.

In a recent breakthrough published in Scientific reports, a team of researchers led by Professor Masato Kotsugi of Tokyo University of Science, Japan, has succeeded in automating the interpretation of microscopic image data. This was achieved using an “extended Landau free energy model” which the team developed using a combination of topology, data science and free energy. The model could illustrate the physical mechanism as well as the critical location of the magnetic effect, and suggest an optimal structure for a nanodevice. The model used physics-based features to draw energy landscapes in information space, which could be applied to understand complex nanoscale interactions in a wide variety of materials.

Conventional analysis is based on visual inspection of images under a microscope, and relationships with material function are expressed only qualitatively, which is a major bottleneck for material design. Our extended Landau free energy model allows us to identify the physical origin and localization of complex phenomena within these materials. This approach makes it possible to overcome the problem of explainability faced by deep learning, which amounts to reinventing new physical laws,says Professor Kotsugi. This work was supported by KAKENHI, JSPS and the MEXT-Program for Creation of Innovative Core Technology for Power Electronics Grant.

While designing the model, the team used the advanced technique in the fields of topology and data science to extend the Landau free energy model. This led to a model that allowed causal analysis of magnetization reversal in nanomagnets. The team then carried out an automated identification of the physical origin and visualization of the original images of the magnetic domain.

Their results indicate that the demagnetization energy near a defect gives rise to a magnetic effect, which is responsible for the “pinning phenomenon”. Additionally, the team was able to visualize the spatial concentration of energy barriers, a feat that had not been achieved until now. Finally, the team proposed a topologically inverse design of the recording devices and low-power nanostructures.

The model proposed in this study is expected to contribute to a wide range of applications in the development of spintronic devices, quantum information technologies and Web 3.

Our proposed model opens up new possibilities for optimizing magnetic properties for materials engineering. The extended method will finally allow us to specify “why” and “where” the function of a material is expressed. Material function analysis, which previously relied on visual inspection, can now be quantified to enable accurate functional design,” concludes an optimistic Professor Kotsugi.



DOI: https://doi.org/10.1038/s41598-022-21971-1

About Tokyo University of Science
Tokyo University of Science (TUS) is a well-known and respected university, and the largest private research university specializing in science in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Founded in 1881, the university has continuously contributed to the scientific development of Japan by instilling a love of science in researchers, technicians and educators.

With a mission to “create science and technology for the harmonious development of nature, human beings and society”, TUS has undertaken a wide range of research from basic science to applied science. TUS took a multidisciplinary approach to research and undertook intensive studies. in some of today’s most vital areas. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel laureate and the only private university in Asia to produce Nobel laureates within the field of natural sciences.

Website: https://www.tus.ac.jp/en/mediarelations/

About Professor Masato Kotsugi of Tokyo University of Science
Professor Masato Kotsugi graduated from Sophia University, Japan in 1996 and later obtained his Ph.D. from the Graduate School of Engineering Science, Osaka University, Japan in 2001. He joined Tokyo University of Science in 2015 as a lecturer and is currently a professor at the Faculty of Advanced Engineering , Department of Materials Science and Technology. Professor Kotsugi and his students are conducting cutting-edge research into high-performance materials to create a green energy society. He has published over 127 peer-reviewed papers and is currently interested in solid-state physics, magnetism, synchrotron radiation, and materials informatics. He can be contacted at: kotsugi@rs.tus.ac.jp

Funding Information
This study was supported by KAKENHI, JSPS [21H04656]. Part of this study was supported by the MEXT-Program for Creation of Innovative Core Technology for Power Electronics Grant Number JPJ009777, and KAKENHI, JSPS [19K22117, 22K14590].

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