Published: 29.6.2026
Physicists have used machine-learning to discover two new superconductors––it represents a substantial step towards realising massive energy efficiency gains from superconductivity.

An international team of quantum researchers has shown how machine learning can be used to filter a practically infinite number of possible material combinations to identify candidates for superconductivity. 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.

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‘Over the decades researchers have recognised 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 if you manage to find what might look like a viable combination, most are completely unusable. For example, they are difficult to synthesize or scale, says Törmä. It follows that finding viable superconductors requires vast computational power to screen materials. SuperC’s machine-learning approach upends that idea.

‘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.’

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