Scientists have demonstrated a powerful new way to search for one of physics’ biggest prizes: practical superconductors.
An international team of researchers has demonstrated a new way to discover superconductors much faster by combining machine learning with advanced quantum physics. The approach allows scientists to sift through an almost limitless number of possible material combinations and pinpoint the most promising candidates for superconductivity.
The breakthrough, led by the SuperC consortium, has already resulted in the discovery of two new superconducting materials. According to Aalto University Professor Päivi Törmä, who leads the collaboration, the method could significantly accelerate the search for new superconductors.
Superconductors can carry electricity with zero electrical resistance because of a quantum effect that appears only at extremely low temperatures. They are essential for technologies including quantum computers, MRI and other neuroimaging systems, fusion reactors, and high-speed maglev trains.
Finding new superconductors, however, is extraordinarily difficult. Nearly endless combinations of chemical elements are theoretically possible, but only a tiny fraction exhibit superconductivity. Even those that have already been discovered require expensive cooling systems to reach temperatures close to absolute zero before they can function.
Researchers around the world are pursuing an even bigger goal: finding a practical superconductor that works at room temperature.
“Superconductive materials that can operate at room temperature would forever change the way we consume energy,” explains Törmä. “If such a material could replace regular conductors in applications like computers and data centers, global energy consumption could be slashed and the heat footprint of the ICT sector vastly reduced.”
AI and Quantum Physics Join Forces
The SuperC consortium was established in 2023 by Professor Törmä and an international group of leading physicists with the goal of using quantum physics to help address climate change. It is the first coordinated global collaboration dedicated to discovering new superconductors, and its ambitious objective is to identify a room-temperature superconductor by 2033.
According to Törmä, the team’s strategy combines quantum geometry with machine learning to dramatically narrow the search.
The newly discovered superconductors, known as YRu3B2 and LuRu3B2, owe their superconducting behavior to electrons forming flat bands within a kagome lattice, a geometric arrangement inspired by traditional Japanese basket weaving patterns.
To find these materials, the researchers first used machine learning to screen vast numbers of possible elemental combinations. A specialized algorithm identified the most promising candidates, which were then examined using detailed theoretical calculations to determine whether they were likely to become superconductors.
Once those predictions were confirmed, collaborators at Rice University synthesized the materials by chemically combining the required elements into new compounds. The effort was led by Professor Emilia Morosan. Laboratory testing then verified that both materials were indeed superconductors.
The proof-of-concept study was recently published in Physical Review Research.
Why Finding Superconductors Is So Challenging
The underlying quantum physics behind superconductivity is extremely complex, making the discovery of new materials a slow and difficult process.
“Over the decades researchers have recognized over 7,000 superconductors, but mostly serendipitously,” explains Törmä. “The process of identifying possible materials is so computationally heavy that, in fact, researchers have only been able to theoretically predict the viability of about 20 of these.”
Even when a material appears promising on paper, it often proves impractical because it is too difficult to manufacture or impossible to scale for real-world applications. Traditionally, screening enough materials to find useful superconductors has required enormous computing resources.
The SuperC team’s approach changes that by using machine learning to eliminate unlikely candidates before performing the most demanding calculations.
“Our method uses machine-learning-based pre-screening followed by targeted calculations on the promising candidates. This approach will greatly speed up superconductor discovery in the future. With machine learning, we may be able to push the number of materials we can process into the billions,” says Törmä. “This will take us a critical step closer to finding a room-temperature superconductor.”
SuperC’s research will be featured in Aalto University’s Designs for a Cooler Planet exhibition from September 1 through October 30, 2026, in Greater Helsinki, Finland.
Reference: “Machine-learning-guided discovery of kagome superconductors YRu3B2 and LuRu3B2” by Rose Albu Mustaf, Sajilesh K. P., Sanu Mishra, Junze Deng, Yi Jiang, Kaja H. Hiorth, Eeli O. Lamponen, Martin Gutierrez-Amigo, Päivi Törmä, Miguel A. L. Marques, B. Andrei Bernevig and Emilia Morosan, 17 June 2026, Physical Review Research.
DOI: 10.1103/lpqj-7hyg
The SuperC consortium is funded by The Kavli Foundation, Klaus Tschira Stiftung, and Kevin Wells, along with the Jane and Aatos Erkko Foundation, the Keele Foundation, the Magnus Ehrnrooth Foundation, and the Neste and Fortum Foundation.
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1 Comment
“Fascinating work by the SuperC consortium. While hunting for exotic elemental recipes to naturally form flat-band kagome structures presents serious scalability challenges for macro-scale utility lines due to material ductility, advanced computing architecture is the absolute ideal venue for this breakthrough.
Depositing these specialized chemical lattices via established vapor deposition techniques onto rigid microchip substrates completely bypasses the brittleness issue. Eliminating electrical resistance at the chip level would revolutionize data infrastructure, solving the thermal dissipation bottleneck and dramatically reducing global data center cooling demands.
For the macro-scale power grid, however, the path forward remains a geometry-first engineering paradigm. Rather than relying on chemical synthesis to achieve atomic configurations over thousands of miles, advancements in Laser-Interference Lithography (LIL) allow us to stamp precise, sub-nanometer geometric valleys and boundary grooves directly into robust, ductile base metals like copper or aluminum. These engineered micro-channels act as physical boundary guides to compress energy flow and limit resistance mechanically.
Geometry dictates behavior. The SuperC flat-band structures are a massive leap forward for the future of microprocessors, while structural dimension modification remains the most scalable path to grid-level efficiency.
Sub-Note: The mechanical interaction between these macro-structural dimensions and micro-atomic lattices—and how spatial constraint directly governs resistance—is comprehensively modeled under the Torsion Hill Unified Framework.”