Causal discovery from ARPES measurement data. Credit: Scientific Reports (2025). DOI: 10.1038/s41598-025-29687-8

Tohoku University and Fujitsu Limited have successfully used AI to derive new insights into the superconductivity mechanism of a new superconducting material.

Their findings demonstrate an important use case for AI technology in new materials development and suggest that the technology has the potential to accelerate research and development. This could drive innovation in various industries such as the environment and energy, drug discovery and health care, and electronic devices.

The AI technology was used to automatically clarify causal relationships from measurement data obtained at NanoTerasu Synchrotron Light Source. This achievement was published in Scientific Reports.

AI platform and collaborative research

The two parties used Fujitsu’s AI platform, Fujitsu Kozuchi, to develop a new discovery intelligence technique to accurately estimate causal relationships. Fujitsu will begin offering a trial environment for this technology in March 2026.

Furthermore, in collaboration with the Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, the two parties applied this technology to data measured by angle-resolved photoemission spectroscopy (ARPES), an experimental method used in materials research to observe the state of electrons in a material, using a specific superconducting material as a sample.

Tohoku University and Fujitsu established the Fujitsu x Tohoku University Discovery Intelligence Laboratory in October 2022 as part of the Fujitsu Small Research Lab initiative, which sees Fujitsu researchers stationed at universities to accelerate joint research, discover new themes, develop human resources, and build long-term relationships.

History of photoemission-data quantity and schematics of causal discovery. Credit: Scientific Reports (2025). DOI: 10.1038/s41598-025-29687-8

Goals for societal impact and innovation

The aim is to contribute to solving societal issues through the development of new technologies and human resource development, by integrating Tohoku University and Fujitsu’s technologies, achievements, and knowledge. The two parties are engaged in joint research to develop and socially implement discovery intelligence that uses AI to find solutions to various problems from data, including those in materials science.

NanoTerasu Synchrotron Light Source, which began operation in April 2024, enables the measurement of molecular, atomic, and electronic states with nanometer-level high spatial resolution.

The facility works to develop new functional materials to drive innovation and contribute to resolving societal issues, including environmental challenges. However, as measurement performance improves, the amount of data created increases.

Efficiently extracting only useful information without relying on human experience or intuition and advancing the automation of scientific research processes are key priorities for the future.

Data challenges and AI-driven solutions

ARPES measurement data is very large. A causal graph of the data has a massive number of nodes, making it difficult to find useful information.

The technique developed in this collaboration significantly compresses the scale of the causal graph by performing fitting based on a model equation for the measurement data and constructing a causal graph from only the extracted parameters. In addition, the two parties developed a technique to further simplify the graphs and reduce noise impact.

This technology reduced the size of the causal graph to less than 1/20 of the conventional size, enabling the efficient discovery of new insights.

Breakthroughs in superconductivity research

Tohoku University and Fujitsu applied this technology to ARPES measurement data of cesium vanadium antimonide (CsV3Sb5), a kagome superconducting material.

Cesium vanadium antimonide has potential applications as a high-temperature superconductor, but its superconductivity mechanism is not yet fully understood. They found that the superconductivity mechanism is due to the interaction of vanadium, antimony, and cesium electrons.

Moving forward, both organizations will further leverage this technology along with NanoTerasu’s world-class capabilities in spatial resolution to automatically clarify the causal relationships between phenomena at the microscopic level.

This will contribute to the development of new functional materials that address global environmental issues—one of Fujitsu’s materiality priorities—in areas such as high-temperature superconductivity and next-generation low-power consumption devices.

More information: K. Fujita et al, Extracting causality from spectroscopy, Scientific Reports (2025). DOI: 10.1038/s41598-025-29687-8

Citation: Promising new superconducting material discovered with the help of AI (2025, December 23) retrieved 23 December 2025 from https://phys.org/news/2025-12-superconducting-material-ai.html

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