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Friday, 3 July 2026dailybeirut.com
Tech & Science
An international team has combined machine learning with quantum physics to identify new superconductors faster, advancing the quest for room-temperature superconductivity.
An international research collaboration has introduced a novel method that accelerates the search for superconductors by integrating machine learning with advanced quantum physics. This technique enables scientists to efficiently evaluate an immense range of potential material combinations and identify the most promising superconducting candidates.
The SuperC consortium, which spearheaded this development, has already discovered two new superconducting compounds. Professor Päivi Törmä of Aalto University, who leads the group, stated that this approach could substantially speed up the discovery process for new superconductors.
Superconductors are materials capable of conducting electricity without resistance due to a quantum phenomenon that manifests only at extremely low temperatures. These materials are critical for various technologies, including quantum computing, MRI and neuroimaging devices, fusion reactors, and high-speed magnetic levitation trains.
Despite their importance, discovering new superconductors remains a formidable challenge. While countless chemical element combinations exist theoretically, only a minuscule portion exhibit superconductivity, and those known typically require costly cooling to temperatures near absolute zero to function effectively.
The global scientific community aims to identify superconductors that operate at room temperature, which would have transformative effects on energy consumption. Professor Törmä explained that room-temperature superconductors could replace conventional conductors in applications like computers and data centers, significantly reducing global energy use and the heat output of the information and communication technology sector.
Founded in 2023 by Professor Törmä and an international team of physicists, the SuperC consortium is the first coordinated global effort dedicated to discovering new superconductors. Its ambitious goal is to find a room-temperature superconductor by 2033.
The consortium’s strategy merges quantum geometry concepts with machine learning to drastically narrow down the search space. The newly identified superconductors, YRu3B2 and LuRu3B2, exhibit superconductivity due to electrons forming flat bands within a kagome lattice—a geometric pattern inspired by traditional Japanese basket weaving.
The researchers initially applied machine learning algorithms to screen a vast array of elemental combinations. The algorithm pinpointed the most promising candidates, which were then subjected to detailed theoretical calculations to assess their superconducting potential.
Following these predictions, collaborators at Rice University, led by Professor Emilia Morosan, synthesized the compounds by chemically combining the necessary elements. Subsequent laboratory tests confirmed that both materials indeed possess superconducting properties.
This proof-of-concept study was published recently in Physical Review Research.
The quantum physics underlying superconductivity is highly complex, which has historically made discovering new materials slow and difficult. Professor Törmä noted that over the decades, more than 7,000 superconductors have been identified, mostly through chance. However, theoretical predictions have only been feasible for about 20 of these materials due to computational limitations.
Even when materials appear promising theoretically, practical issues such as manufacturing difficulties or scalability often hinder their real-world application. Traditional screening methods require vast computational resources to evaluate enough materials to find useful superconductors.
The SuperC consortium’s approach addresses this by employing machine learning to pre-screen and exclude unlikely candidates before conducting intensive calculations on the most promising materials. Professor Törmä stated that this method will significantly accelerate superconductor discovery and could enable processing billions of materials, bringing researchers closer to finding a room-temperature superconductor.
The SuperC consortium’s research will be showcased in Aalto University’s Designs for a Cooler Planet exhibition, scheduled from September 1 to October 30, 2026, in Greater Helsinki, Finland.
The consortium receives funding from The Kavli Foundation, Klaus Tschira Stiftung, Kevin Wells, the Jane and Aatos Erkko Foundation, the Keele Foundation, the Magnus Ehrnrooth Foundation, and the Neste and Fortum Foundation.
Reference: “Machine-learning-guided discovery of kagome superconductors YRu3B2 and LuRu3B2” by Rose Albu Mustaf et al., published June 17, 2026, in Physical Review Research. DOI: 10.1103/lpqj-7hyg
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