Scientists utilize Uniform Manifold Approximation and Projection to analyze the X-ray absorption spectroscopy data. Credit: Professor Masato Kotsugi from Tokyo University of Science, Japan

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 a 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 and 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 toward 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.

The team first generated XAS data for three different phases of boron nitride (BN) with different atomic structures, along with their defect analogs. 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.

The proposed approach uses dimensionality-reduction-based machine learning methods to automatically analyze complex XAS data. Credit: Professor Masato Kotsugi from Tokyo University of Science, Japan

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 demonstrates 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 already been applied to different experimental datasets. In the near future, this approach will 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.

More information: Automated Elucidation of Crystal and Electronic Structures in Boron Nitride from X-ray Absorption Spectra Using Uniform Manifold Approximation and Projection, Scientific Reports (2025). DOI: 10.1038/s41598-025-18580-z

Citation: Machine learning automates material analysis and design using X-ray spectroscopy data (2025, November 10) retrieved 10 November 2025 from https://phys.org/news/2025-11-machine-automates-material-analysis-ray.html

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