Post by Aalto University

🔬Researchers identified two new superconductors with a machine-learning-based method —a substantial step towards unlocking massive energy efficiency gains from superconductivity. Both of the newly discovered materials gain their superconductivity from electrons forming flat bands in a traditional pattern known as a kagome lattice. 💬Thanks to the breakthrough, new superconductors can now be found much faster, says Aalto University Professor Päivi Törmä, who leads the SuperC consortium behind the research. ⚡Superconductors carry electric current with zero resistance because of a quantum effect appearing only at extremely low temperatures. They power not only quantum computers, but many other things, from neuroimaging to fusion reactors and maglev trains. 💬‘If such a material could replace regular conductors in applications like computers and data centres, global energy consumption could be slashed and the heat footprint of the ICT sector vastly reduced,’ Törmä says. While more than 7,000 superconductors have been identified over the decades, most were found by chance. SuperC’s approach of machine learning and targeted calculations enables researchers to screen vast numbers of potential materials far more efficiently. 💬“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.’ 📷: Esa Kapila