AI and big data roadmap could enable superconductor revolution

A new roadmap which lays out how AI and big data techniques could drive advances in superconductivity research and development is aiming to help to spark a tech revolution.

An international team of leading engineers, physicists and computing scientists are behind the roadmap, which is the first of its kind for the field of superconductor research.

Dr Mohammad Yazdani-Asrami of the University of Glasgow led the production of the roadmap, which is published as an invited paper in the Institute of Physics’ journal, Superconductor Science and Technology.

The paper showcases 18 short articles, produced by 40 researchers from 25 institutions around the world. Together, they offer a comprehensive guide to how the power of machine learning could help overcome challenges which have held back the creation of new technologies built with superconducting components.

Superconductors are a unique group of lightweight materials which can generate strong magnetic fields and transfer or store large amounts of energy. They are also capable of conducting electricity with zero resistance, a property which sets them apart from all other conductive materials, which lose energy as heat when current flows through them.

Superconductors are currently used in magnetic resonance imaging, or MRI, which has enabled major advances in medical and cancer diagnostics by creating detailed scans of the body. They have also underpinned promising advancements in particle accelerators, high-performance computing, energy storage and more.

In the future, new superconductor technologies could also create breakthroughs in wind power generation, fusion energy, electric and hydrogen-powered transport, and aerospace applications helping the world achieve net-zero.

However, a series of tough challenges have so far prevented the widespread adoption and commercialisation of superconducting technology across the full spectrum of industries. Aside from MRIs, there are currently very few superconducting devices in commercial use, with many still confined to research facilities.

Part of that is because industrial superconductor production is difficult, energy-intensive and expensive - an issue which is compounded by the need to cool the materials to temperatures far below zero for them to operate at peak efficiency.

Each section in the roadmap posits how new developments in AI and big data could help overcome problems currently holding back the development of specific areas of superconductor research.

The authors outline the challenges facing superconductor research in material design, manufacturing, testing, operation and condition monitoring and demonstrate how AI could help develop new approaches to solving them.

Those challenges include:

  • the optimal design optimization of superconducting propulsion systems in hydrogen-electric aircraft
  • fault detection in superconducting devices
  • hot spot detection in superconducting devices for fusion applications
  • real-time and surrogate modelling of superconducting systems
  • quench detection of superconducting magnets
  • new superconductor discovery
  • superconductor manufacturing

Dr Yazdani-Asrami, of the University of Glasgow’s James Watt School of Engineering, led the team of authors and co-ordinated the drafting and editing of the paper. He is a named author on four of its sections.

He said: "Superconductors have enabled some truly remarkable technologies over the last few decades and hold the promise of underpinning many more in the decades to come.

"Artificial intelligence and machine learning have already proven their value in many areas of science and engineering. They are invaluable for sifting through huge amounts of data, finding hidden patterns and making decisions that can help bolster human ingenuity.

"The roadmap is a true international collaboration, with input from experts in Europe, North and South America, Asia, and Oceania. It’s the first time that experts from a diverse range of disciplines have worked together to forecast how AI can advance cryogenic and superconducting technology towards commercialisation, which is where real change will happen.

"Our goal in putting the paper together was to inspire researchers from a wide range of fields to embrace the potential that AI and big data techniques have to create new opportunities for superconducting materials and technologies."

Dr Wenjuan Song, also from the University of Glasgow’s James Watt School of Engineering, co-authored the roadmap. She added: "The roadmap also aims to help policymakers and industry to recognise that they also have a part to play in making these breakthroughs possible.

"I’m excited to see how our roadmap is received by the superconductor research and development and beyond. I’m also looking forward to doing my part in driving forward superconductor advances in electrically-powered aircraft, my own field of research."

The team’s paper, titled ’Roadmap on Artificial intelligence and big data techniques for superconductivity’ , is published in Superconductor Science and Technology and is available free of charge