Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy (XAS) is an important technique for this, as it reveals detailed insights about a material’s composition, structure, and functional characteristics. The technique works by directing a beam of high-energy X-rays at a sample and recording how X-rays of different energy levels are absorbed. Similar to how white light splits into a rainbow after passing through a prism, XAS produces a spectrum of X-rays with different energies. This spectrum is called as spectral data, which acts like an unique fingerprint of a material, helping scientists to identify the elements present in the material and see how the atoms are arranged. This information, known as the ‘electronic state,’ determines the functional properties of materials.
Boron compounds have significant applications in semiconductors, Internet-of-Things (IoT) devices, and energy storage. In these materials, atomic modifications, structural defects, impurities, and doped elements, each produce unique, complex variations in spectral data. Detailed analyses of these variations provides key insights into their electronic state and is crucial for rational material design. Traditionally however, such analyses required extensive expertise and manual labor, especially when large datasets have to be examined visually.
The lack of prior reference data subjectivity of interpretations made the task even more difficult. Developing an automated approach that can establish a clear and objective link between XAS data and the underlying material properties has been a longstanding challenge.
Now, a research team headed by Professor Masato Kotsugi from the Department of Material Science and Technology at Tokyo University of Science (TUS), Japan, has taken a promising step towards this goal. Together, Ms. Reika Hasegawa and Dr. Arpita Varadwaj, both from TUS and who led the study, developed an automated artificial intelligence (AI)-based approach for analyzing XAS data. “AI-based data-driven methods, such as machine learning, can be powerful tools for efficiently analyzing and interpreting measurement data, providing objective insights,” explains Prof. Kotsugi. The study was published in the journal Scientific Reports on 10th of November 2025.
The team first generated XAS data for three different phases of boron nitride (BN) with different atomic structures, along with their defect analogues. The XAS data were generated using theoretical calculations based on fundamental physics and validated using experimental data.
To analyze this data, the team then employed machine learning techniques that use dimensionality reduction. In this method, highly complex data with many variables is reduced to its fundamental elements, capturing only its essential features. In XAS, where a dataset can have thousands of variables, machine learning helps scientists focus on patterns that truly reflect the materials’ electronic states. As Prof. Kotsugi explains, “The underlying physics in XAS data can be explained by only a few mathematical calculations.” The team tested four machine learning methods: Principal Component Analysis (PCA), Multidimensional Scaling (MDS), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP).
Among them, UMAP performed exceptionally well in classifying complex spectral data according to different atomic structures and defects. It was able not only to identify global trends, but also to detect subtle differences between phases and defect types. To confirm its validity, the researchers compared these results using experimental XAS data, which closely matched the classifications derived by UMAP, despite the presence of noise and variability. This demonstrate that this method is robust against noise and variations introduced by experimental conditions. “Our findings show that UMAP can be a valuable tool for rapid, scalable, automated, and importantly, objective material identification using complex experimental spectral data,” remarks Prof. Kotsugi.
Notably, this study represents a more advanced method compared to the team’s previous statistical similarity-based approach. While that method was accurate, this new AI-based method exhibits even higher accuracy and can also reveal meaningful variations in electronic states.
Highlighting the study’s impact, Prof. Kotsugi says, “Our method demonstrates the potential of autonomous structural identification, opening up new possibilities for data-driven material design and development of novel materials.” The AI-based approach has been already applied to different experimental datasets. In the near future, this approach would be implemented as software at the Nano-Terasu synchrotron radiation center. Looking ahead, this innovative AI-based approach will accelerate the development of new materials, advancing key fields like semiconductors, catalysis, and energy storage, helping to build a more sustainable future.
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Reference
DOI: https://doi.org/10.1038/s41598-025-18580-z
About The Tokyo University of Science
Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan's development in science through inculcating the love for science in researchers, technicians, and educators.
With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society," TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today's most vital fields. 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 Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.
Website: https://www.tus.ac.jp/en/mediarelations/
About Professor Masato Kotsugi from Tokyo University of Science
Professor Masato Kotsugi graduated from Sophia University, Japan, in 1996 and subsequently received his Ph.D. from the Graduate School of Engineering Science at Osaka University, Japan, in 2001. He joined Tokyo University of Science in 2015 as a lecturer and is now a Professor at the Faculty of Advanced Engineering, Department of Materials Science and Technology. Prof. Kotsugi and his students conduct cutting-edge research on high-performance materials to create a green-energy society. He has published over 130 peer-reviewed papers and is currently interested in solid-state physics, magnetism, synchrotron radiation, and materials informatics.
Funding information
The team would like to thank Institute of Molecular Science, Okazaki, Japan for supercomputing facilities received for calculations (Project: 23-IMS-C137), and all authors thank CREST project for generous funding (JPMJCR21O4).
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