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The SuperC consortium, led by Aalto University Professor Päivi Törmä, is using machine learning to identify room-temperature superconductors by 2033. By combining quantum geometry with AI screening, the team recently identified two new superconducting materials, YRu3B2 and LuRu3B2, which were experimentally verified by researchers at Rice University.

How is AI accelerating the discovery of superconductors?

Traditional superconductor discovery is often a matter of chance. According to Professor Törmä, researchers have identified over 7,000 superconductors over several decades, but most were found serendipitously. The computational burden of predicting these materials is so high that scientists have only theoretically predicted the viability of about 20 of them.

The SuperC approach flips this model. The team uses machine learning to rapidly pre-screen enormous numbers of possible elemental combinations. A specialized algorithm filters out unlikely candidates, leaving only the most promising materials for detailed quantum calculations. This targeted method prevents researchers from wasting massive computing resources on materials that won’t work.

What happens if a room-temperature superconductor is found?

Most current superconductors only function when cooled to temperatures near absolute zero using expensive cooling systems. A material that works at room temperature would eliminate this requirement, fundamentally changing global energy infrastructure.

Professor Törmä states that replacing regular conductors in computers and data centers with room-temperature superconductors could slash global energy consumption and drastically reduce the heat footprint of the ICT sector. This shift would impact several high-energy technologies already relying on superconductivity, including:

• Medical Imaging: Neuroimaging systems.
• Transportation: Maglev trains.
• Energy: Fusion reactors.
• Computing: Quantum computers.

Who is behind the SuperC consortium?

Established in 2023, SuperC is the first coordinated global collaboration dedicated to finding a room-temperature superconductor. The group consists of international physicists focused on using quantum physics to combat climate change.

The workflow involves a three-step verification process:

1. AI Screening: Machine learning identifies candidates.
2. Theoretical Analysis: Quantum calculations confirm potential.
3. Physical Synthesis: A team at Rice University, led by Professor Emilia Morosan, chemically combines elements to create and verify the material.

The results of this proof-of-concept study were published in Physical Review Research.

Why is the search for these materials so difficult?

The chemical search space is virtually endless. Even when a material looks promising in a simulation, it may be impossible to produce at scale or too difficult to synthesize in a lab. By focusing detailed calculations only on the strongest candidates, the SuperC team believes they may be able to process materials in the billions, moving the field closer to the 2033 goal.

The consortium’s work is supported by a variety of organizations, including the Kavli Foundation, Klaus Tschira Stiftung, the Jane and Aatos Erkko Foundation, and the Neste and Fortum Foundation, among others.

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