Researchers identify new superconductor and uncover process that could create thousands more

An international team of quantum researchers has shown how machine learning can be used to filter through a virtually infinite number of possible material combinations to identify superconducting candidates. A breakthrough allows us to find new superconductors much faster, says Aalto University professor. paivi tormais led by Super C A consortium that supports research.

Superconductors carry electrical current with zero resistance thanks to quantum effects that only appear at extremely low temperatures. These will power not only quantum computers, but many other things, from neuroimaging to fusion reactors and maglev trains.

However, these unicorn materials are very difficult to identify. Any infinitely variable combination of elements can become a superconductor, but very few actually become superconductors. And those already discovered require expensive cooling equipment to bring them to temperatures close to absolute zero, which gives them quantum properties.

Scientists around the world are in the race to find scalable superconductors that operate at room temperature.

“Superconducting materials that can operate at room temperature will forever change the way we consume energy,” Tolma explains. “If such materials can replace ordinary conductors in applications such as computers and data centers, it could reduce global energy consumption and significantly reduce the thermal footprint of the ICT sector.”

Reaching proof of concept

Driven by a shared desire to use quantum physics to fight climate change, Professor Tolma and a team of eminent physicists founded the SuperC consortium in 2023. This is the first global collaborative effort to discover new superconductors and aims to discover room-temperature superconductors by 2033.

According to Törmä, SuperC’s combination of quantum geometry and machine learning is a great starting point. This latest discovery is based on traditional Japanese basket-making patterns. Both newly discovered substances (YRu)₃B₂ and lulu₃B₂) obtains superconductivity from electrons forming flat bands in a traditional pattern known as a kagome lattice.

To identify the two new superconductors, the team used machine learning to narrow down promising combinations of elements. After pre-screening these with a proprietary algorithm, the team performed detailed calculations to determine which materials had the potential to become superconducting.

After theoretical confirmation, the SuperC collaborators at Rice University set out to synthesize the sample. This complex process of chemically combining raw elements to create new compounds was led by the professor. Emilia Morosan. The Rice University team was then able to perform tests to confirm the superconductivity of the material.

This proof-of-concept paper recently physical review study.

Why is it important?

Discovering new superconductors is a difficult task because the quantum mechanical theory of superconductivity is complex.

“Over the decades, researchers have recognized more than 7,000 superconductors, most by chance,” Tolma explains. “The process of identifying potential substances is so computationally intensive that in fact, researchers have only been able to theoretically predict the viability of about 20 of them.”

Even if you do manage to find combinations that seem workable, most are completely unusable. For example, it’s difficult to composite and scale, Tolma says. Finding viable superconductors therefore requires enormous computational power to sort through materials. SuperC’s machine learning approach turns that idea on its head.

“Our method uses machine learning-based pre-screening followed by targeted calculations on promising candidates. This approach will greatly accelerate the discovery of future superconductors. Using machine learning, we may be able to push the number of materials that can be processed into the billions,” Tolma says. “This brings us much closer to discovering room-temperature superconductors.”

SuperC research will be presented in a paper from Aalto University. Designing for a cooler planet The exhibition will be held from September 1st to October 30th, 2026 in the Helsinki metropolitan area, Finland.

The SuperC consortium is funded by the Kavli Foundation, Klaus Tschira Stiftung, Kevin Wells, Jane & Aatos Erkko Foundation, Kele Foundation, Magnus Ehrnrootth Foundation, and Neste and Fortum Foundation.