With pulses of sound through tiny speakers, Cornell physics researchers have clarified the basic nature of a new superconductor.Since it was found to be a superconductor about five years ago, uranium ditelluride has created a lot of buzz in the quantum materials community – and a lot of confusion, with more than a dozen theories about the true nature of its superconducting properties. Some suggested valuable possibilities for quantum computing.

How do you know you are looking at a dog? What are the odds you are right? If you’re a machine-learning algorithm, you sift through thousands of images – and millions of probabilities – to arrive at the “true” answer, but different algorithms take different routes to get there. A collaboration between researchers from Cornell and the University of Pennsylvania has found a way to cut through that mindboggling amount of data and show that most successful deep neural networks follow a similar trajectory in the same “low-dimensional” space. “Some neural networks take different paths. They go at different speeds. But the striking thing is they’re all going the same way,” said James Sethna, professor of physics in the College of Arts and Sciences, who led the Cornell team.

Ranga Dias claimed to have discovered the first room-temperature superconductors, but the work was later retracted. An investigation by Nature’s news team reveals new details about what happened — and how institutions missed red flags.