For decades, finding a new superconductor was mostly luck.

Over 7,000 have been identified since the phenomenon was first observed in 1911, but the vast majority were discovered serendipitously — a physicist trying one more material combination, hoping for a breakthrough. The number of possible elemental combinations is so vast that brute-force searching was never an option.

That era may have just ended.

An international team led by Aalto University in Finland has demonstrated that machine learning can systematically hunt for superconductors, and the proof arrived in late June: two previously unknown materials, YRu₃B₂ and LuRu₃B₂, both confirmed to carry electricity with zero resistance.

What makes the discovery especially elegant is the geometry. Both materials owe their superconducting properties to electrons arranged in a kagome lattice, a hexagonal weave pattern named after traditional Japanese basket-making. The same pattern that has held bamboo together for centuries now holds the key to one of physics' hardest problems.

The team didn't just find two new materials. They built a workflow. Machine learning pre-screens candidate combinations from a near-infinite chemical space, quantum calculations narrow the list, and experimental collaborators — in this case, Professor Emilia Morosan's lab at Rice University — synthesize and test the survivors.

"This approach will greatly speed up superconductor discovery in the future," said Professor Päivi Törmä, who leads the SuperC consortium behind the research. The consortium, launched in 2023, has set an ambitious target: find a room-temperature superconductor by 2033.

If they succeed, the implications are hard to overstate. Superconductors that work without expensive cryogenic cooling could replace conventional conductors in power grids, data centers, and computing hardware. "Global energy consumption could be slashed and the heat footprint of the ICT sector vastly reduced," Törmä said.

The paper was published in Physical Review Research. And the researchers are clear-eyed about what comes next: two materials is a proof of concept. Thousands more may be waiting.

Sources: Aalto University, Interesting Engineering