New Superconducting Material For Levitating High-Speed Train or to Achieve Nuclear Fusion

In a historic achievement, University of Rochester researchers have created a superconducting material at both a temperature and pressure low enough for practical applications.

With this material, the dawn of ambient superconductivity and applied technologies has arrived,” according to a team led by Ranga Dias, an assistant professor of mechanical engineering and of physics. In a paper in Nature, the researchers describe a nitrogen-doped lutetium hydride (NDLH) that exhibits superconductivity at 69 degrees Fahrenheit (26 degrees Celsius) and 10 kilobars (145,000 pounds per square inch, or psi) of pressure.

Although 145,000 psi might still seem extraordinarily high (pressure at sea level is about 15 psi), strain engineering techniques routinely used in chip manufacturing, for example, incorporate materials held together by internal chemical pressures that are even higher.

Scientists have been pursuing this breakthrough in condensed matter physics for more than a century. Superconducting materials have two key properties: electrical resistance vanishes, and the magnetic fields that are expelled pass around the superconducting material. Such materials could enable:

  • Power grids that transmit electricity without the loss of up to 200 million megawatt hours (MWh) of the energy that now occurs due to resistance in the wires
  • Frictionless, levitating high-speed trains
  • More affordable medical imaging and scanning techniques such as MRI and magnetocardiography
  • Faster, more efficient electronics for digital logic and memory device technology
  • Tokamak machines that use magnetic fields to confine plasmas to achieve fusion as a source of unlimited power

Previously, the Dias team reported creating two materialscarbonaceous sulfur hydride and yttrium superhydride—that are superconducting at 58 degrees Fahrenheit (14,4 degrees Celsius) /39 million psi and 12 degrees Fahreneheit/26 million psi respectively, in papers in Nature and Physical Review Letters.


How to Predict Stress at Atomic Scale

The amount of stress a material can withstand before it cracks is critical information when designing aircraft, spacecraft, and other structures. Aerospace engineers at the University of Illinois Urbana-Champaign used machine learning for the first time to predict stress in copper at the atomic scale.

According to Huck Beng Chew and his doctoral student Yue Cui, materials, such as copper, are very different at these very small scales.

Left: Machine learning based on artificial neural networks as constitutive laws for atomic stress predictions. Right: Quantifying the local stress state of grain boundaries from atomic coordinate information

Metals are typically polycrystalline in that they contain many grains,” Chew said. “Each grain is a single crystal structure where all the atoms are arranged neatly and very orderly.  But the atomic structure of the boundary where these grains meet can be very complex and tend to have very high stresses.”

These grain boundary stresses are responsible for the fracture and fatigue properties of the metal, but until now, such detailed atomic-scale stress measurements were confined to molecular dynamics simulation models. Using data-driven approaches based on machine learning enables the study to quantify, for the first time, the grain boundary stresses in actual metal specimens imaged by electron microscopy.

“We used molecular dynamics simulations of copper grain boundaries to train our machine learning algorithm to recognize the arrangements of the atoms along the boundaries and identify patterns in the stress distributions within different grain boundary structures,” Cui said. Eventually, the algorithm was able to predict very accurately the grain boundary stresses from both simulation and experimental image data with atomic-level resolution.

We tested the accuracy of the machine learning algorithm with lots of different grain boundary structures until we were confident that the approach was reliable,” Cui explained. The task was more challenging than they imagined, and they had to include physics-based constraints in their algorithms to achieve accurate predictions with limited training data.

When you train the machine learning algorithm on specific grain boundaries, you will get extremely high accuracy in the stress predictions of these same boundaries,” Chew said, “but the more important question is, can the algorithm then predict the stress state of a new boundary that it has never seen before?” For Chew , the answer is yes, and very well in fact.