Superconducting magnets produce strong, stable magnetic fields without the need for large amounts of power, which makes them suitable for technologies such as MRI machines that require a strong magnetic field to produce 3D images of soft tissue. They can also be used in the next generation of transport, including the SCMaglev train system in Japan.
A drawback is that the superconductors currently used are primarily in the form of large coils of superconducting niobium-tin alloy wire and the devices using them need to accommodate this size.
Now, King’s College London (KCL), Tokyo University of Agriculture and Technology, the Japan Science and Technology Agency, the National Institute for Materials Science and Kyushu University have fabricated a cheap and powerful iron-based superconducting magnet using machine learning (ML), which could lead to widespread and affordable use of the technology. The team’s findings have been published in NPG Asia Materials.
In a statement, Dr Mark Ainslie from King’s College London said: “Superconducting magnets are the backbone of the future. Not only are they used to image cancers with MRI machines, but they will be vital for electric aircraft and nuclear fusion. However, the materials and technology required to create traditional copper-based wire superconductors are typically expensive, which has resulted in limited market penetration. Using them in bulk form, as a magnet that doesn’t lose its magnetism once magnetised, can result in a smaller footprint in comparison to heavier coils of wire, but copper-based bulk superconductors can take weeks to fabricate.
“Using artificial intelligence [AI], we’ve produced a cost-effective and scalable alternative using iron, which is a lot easier to work with and opens the door for smaller and lighter weight devices. The first iron-based superconductors were made over ten years ago, but the magnetic fields they produced were nowhere near strong or stable enough for widespread use.
“While superconducting magnets still need to be cooled to very low temperatures to function effectively, our process lays the groundwork for manufacturers to make them at speed and powerful enough for industrial applications. By reducing the need for large amounts of superconducting wire in MRI machines, we can also create a new generation of smaller units that could be deployed at a GP’s office.”
MRI machines have strict requirements for the strength and stability of the magnetic field their magnets produce and the team’s prototype is claimed to be the first iron-based bulk superconductor that meets these requirements. The prototype is also said to have displayed higher levels of thermal stability.
Machine learning
Using an ML system called BOXVIA, the scientists developed a framework that could optimise superconductor creation in the lab faster than previously possible.
Trained on researchers’ attempts to improve the superconductive properties of magnets by shifting parameters like heat and time in the fabrication process, BOXVIA identifies patterns that improve performance and fine tunes parameter changes to come up with an optimal design. Ordinarily, it would take researchers months to create each magnet and test its properties to optimise it for different scenarios.
According to KCL, the researchers also found that superconducting magnets developed with this ML system had a different structure at a microscopic level than those produced without BOXVIA, with larger iron-based crystals within the magnet structure.
The structure of the samples produced by AI was different to high-performing samples created by humans. These samples had a wide range of sizes of iron-based crystals, opposed to the uniform structure that human researchers have traditionally favoured.
The team will now connect how this never-before-seen nanostructure contributes to superior superconducting properties, which is expected to lead to even more powerful magnets.
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