Physicists have used machine-learning to discover two new superconductors––it represents a substantial step towards realising massive energy efficiency gains from superconductivity.
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.
Superconductors carry electric current with zero resistance, thanks to 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.
However, these unicorn materials are prohibitively hard to identify. Any endlessly variable combination of elements could be a superconductor––yet, few actually are. And the ones already discovered require expensive cooling equipment to bring them to the near absolute zero temperatures that give them their quantum properties.
Read More: Aalto University
