\(\mathrm{YRu}_3\mathrm{B}_2\) and \(\mathrm{Lu}_3\mathrm{B}_2\) gain their superconductivity from electrons forming flat bands in a kagome lattice, named after a hexagonal Japanese basket-weaving pattern.
Photo Credit: Esa Kapila
Scientific Frontline: Extended "At a Glance" Summary: Machine Learning in Superconductor Discovery
The Core Concept: Researchers have utilized machine-learning algorithms to identify two new superconductive materials, \(\mathrm{YRu}_3\mathrm{B}_2\) and \(\mathrm{Lu}_3\mathrm{B}_2\), demonstrating a novel methodology to rapidly filter practically infinite elemental combinations. The superconductivity of these materials arises from electrons forming flat bands within a specific geometric atomic structure.
Key Distinction/Mechanism: Unlike traditional superconductor discovery, which has historically relied on serendipity or computationally exhaustive processes, this new framework deploys a machine-learning-based pre-screening process to filter billions of candidates before executing targeted calculations and physical synthesis.
Major Frameworks/Components:
- Machine-Learning Pre-screening: Advanced algorithms capable of computationally processing and filtering billions of potential elemental combinations to find viable material candidates.
- Quantum Geometry: The theoretical and mathematical foundation used to model the quantum properties and viability of the pre-screened combinations.
- Kagome Lattice: A distinct structural atomic arrangement, mirroring a traditional Japanese hexagonal basket-weaving pattern, that facilitates the flat electron bands necessary for superconductivity in \(\mathrm{YRu}_3\mathrm{B}_2\) and \(\mathrm{Lu}_3\mathrm{B}_2\).
Branch of Science: Condensed Matter Physics, Quantum Physics, Materials Science, and Computational Physics.
Future Application: This high-speed algorithmic filtering paves the way for the eventual discovery of a scalable, room-temperature superconductor, which could be implemented in quantum computers, magnetic levitation (maglev) trains, and advanced fusion reactors.
Why It Matters: Currently, superconductors require expensive cooling equipment to reach near-absolute-zero temperatures. Identifying a room-temperature superconductor would revolutionize global power grids, drastically reduce the thermal footprint of the information and communications technology sector, and significantly slash global energy consumption in the fight against climate change.
Physicists have used machine learning to discover two new superconductors—representing a substantial step toward realizing 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 also many other applications, 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. Furthermore, the ones already discovered require expensive cooling equipment to bring them to the near-absolute-zero temperatures that give them their quantum properties.
For scientists the world over, the race is on to find a scalable superconductor that works at room temperature.
"Superconductive materials that can operate at room temperature would forever change the way we consume energy," explains Törmä. "If such a material could replace regular conductors in applications like computers and data centers, global energy consumption could be slashed, and the heat footprint of the ICT sector vastly reduced."
Arriving at Proof of Concept
Driven by a shared desire to harness quantum physics in the fight against climate change, Professor Törmä and a team of renowned physicists formed the SuperC consortium in 2023. It is the first coordinated global collaboration to find new superconductors—and they aim to find a room-temperature superconductor by 2033.
According to Törmä, SuperC’s combination of quantum geometry and machine learning gives it an excellent starting point. This latest discovery has its underpinning in traditional Japanese basket-making patterns; both of the newly discovered materials (\(\mathrm{YRu}_3\mathrm{B}_2\) and \(\mathrm{Lu}_3\mathrm{B}_2\)) gain their superconductivity from electrons forming flat bands in a traditional pattern known as a kagome lattice.
"Our method uses machine-learning-based prescreening followed by targeted calculations on the promising candidates. This approach will greatly speed up superconductor discovery in the future."
Professor Päivi Törmä
To identify the two new superconductors, the team used machine learning to narrow down promising elemental combinations. After prescreening these with a unique algorithm, the team carried out detailed calculations to determine which materials could be superconductive.
After theoretical confirmation, SuperC collaborators at Rice University set about synthesizing the samples. This complex process, which involves chemically combining raw elements into new compounds, was led by Professor Emilia Morosan. The team at Rice was then able to run tests on the materials to confirm their superconductivity.
Why Does It Matter?
The quantum mechanical theory of superconductivity is complex, which makes finding new superconductors an arduous task.
"Over the decades, researchers have recognized more than 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 researchers 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 prescreening 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."
Funding: The SuperC consortium is funded by the Kavli Foundation, Klaus Tschira Stiftung, and Kevin Wells, as well as the Jane and Aatos Erkko Foundation, the Keele Foundation, the Magnus Ehrnrooth Foundation, and the Neste and Fortum Foundation.
Published in journal: Physical Review Research
Authors: Rose Albu Mustaf, Sajilesh K. P., Sanu Mishra, Junze Deng, Yi Jiang, Kaja H. Hiorth, Eeli O. Lamponen, Martin Gutierrez-Amigo, Päivi Törmä, Miguel A. L. Marques, B. Andrei Bernevig, and Emilia Morosan
Source/Credit: Aalto University
Edited by: Scientific Frontline
Reference Number: phy062926_01
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