Story perspectives

AI Breakthrough Accelerates Material Identification Using X-ray Data

11/11/2025

38 5

Summary

Story summary

A research team from Tokyo University of Science in Japan, led by Professor Masato Kotsugi, developed an AI-based method to analyze X-ray Absorption Spectroscopy data.
The approach uses machine learning, specifically Uniform Manifold Approximation and Projection (UMAP), to classify complex XAS spectra.
Their findings were published on November 10, 2025, and indicate that UMAP can facilitate rapid and objective material identification.
The method could accelerate the development of new materials in fields such as semiconductors and energy storage.

1 of 1

Story summary

A research team from Tokyo University of Science in Japan, led by Professor Masato Kotsugi, developed an AI-based method to analyze X-ray Absorption Spectroscopy data.
The approach uses machine learning, specifically Uniform Manifold Approximation and Projection (UMAP), to classify complex XAS spectra.
Their findings were published on November 10, 2025, and indicate that UMAP can facilitate rapid and objective material identification.
The method could accelerate the development of new materials in fields such as semiconductors and energy storage.