Superconducting magnet KCL
In a possible breakthrough for affordable magnetic resonance imaging (MRI) machines and the electrified transport systems of the future, scientists have developed the world’s strongest iron-based superconducting magnet using artificial intelligence.
The major benefit of superconducting magnets is that they can produce very strong and stable magnetic fields without extensive power needs.
This enables their use in several technologies such as MRIs which use the magnetic field to produce clear and 3-dimensional photos of soft tissue.
The researchers from King’s College London and Japan have fabricated the cheap and powerful iron-based superconducting magnet using machine learning (ML), which can pave the way for widespread and affordable use of the technology.
Dr Mark Ainslie from the King’s Department of Engineering collaborated on the project with researchers from Tokyo University of Agriculture and Technology, the Japan Science and Technology Agency, the National Institute for Materials Science and Kyushu University.
The superconducting magnet
The project resulted in the creation of a superconducting magnet which has a “magnetic field that is 2.7 times stronger than that previously reported.”
Using a new machine learning system called BOXVIA; the scientists developed a framework that could optimize superconductor creation in the lab faster than before.
BOXVIA spotted patterns that improve performance and fine-tuned parameter changes to come up with the most optimal design for the magnet. The process would have otherwise taken months to create and then test its properties.
The researchers also discovered that the superconducting magnet they had made using the machine learning system had larger iron based crystals within the magnet structure. This is remarkably different from the structure of magnets made without BOXVIA.
The sample produced by AI was remarkably different as it had wide range of sizes of iron-based crystals, as opposed to the uniform structure that is favored by researchers.
“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,” said Ainslie.
“However, the materials and technology required to create traditional copper-based wire superconductors are typically expensive, which has resulted in limited market penetration,” he added.
Ainslie went on to add that 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.”
The magnet’s use cases
According to Ainslie, the research lays down the groundwork for making superconducting magnets that are powerful enough for industrial applications at speed.
“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,” he said.
“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.”
According to him, this will make the MRI machines cheaper and could also lead to the creation of a “new generation of smaller (MRI) units that could be deployed at a GP’s office, rather than requiring large rooms in hospitals, widening accessibility.”
Abstract
Iron-based high-temperature (high-Tc) superconductors have good potential to serve as materials in next-generation superstrength quasipermanent magnets owing to their distinctive topological and superconducting properties. However, their unconventional high-Tc superconductivity paradoxically associates with anisotropic pairing and short coherence lengths, causing challenges by inhibiting supercurrent transport at grain boundaries in polycrystalline materials. In this study, we employ machine learning to manipulate intricate polycrystalline microstructures through a process design that integrates researcher- and data-driven approaches via tailored software. Our approach results in a bulk Ba0.6K0.4Fe2As2 permanent magnet with a magnetic field that is 2.7 times stronger than that previously reported. Additionally, we demonstrate magnetic field stability exceeding 0.1 ppm/h for a practical 1.5 T permanent magnet, which is a vital aspect of medical magnetic resonance imaging. Nanostructural analysis reveals contrasting outcomes from data- and researcher-driven processes, showing that high-density defects and bipolarized grain boundary spacing distributions are primary contributors to the magnet’s exceptional strength and stability.
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Abhishek Bhardwaj Abhishek brings a wealth of experience in covering diverse stories across different beats. Having contributed to renowned wire agencies and Indian media outlets like ANI and NDTV, he is keenly interested in Tech, Business and Defense coverage.
