Put a tray of water in the freezer. For a while, it’s liquid. And then—boom—the molecules stack into little hexagons, and you’ve got ice. Pour supercold liquid nitrogen onto a wafer of yttrium barium copper oxide, and suddenly electricity flows through the compound with less resistance than beer down a college student’s throat. You’ve got a superconductor.
Those drastic alterations in physical properties are called phase transitions, and physicists love them. It’s as if they could spot the exact instant Dr. Jekyll morphs into Mr. Hyde. If they could just figure out exactly how the upstanding doctor’s body metabolized the secret formula, maybe physicists could understand how it turns him evil. Or make more Mr. Hydes.
A human physicist might never have the neural wetware to see a phase transition, but now computers can. In two papers published in Nature Physics today, two independent groups of physicists—one based at Canada’s Perimeter Institute, the other at the Swiss Federal Institute of Technology in Zurich—show that they can train neural networks to look at snapshots of just hundreds of atoms and figure out what phase of matter they’re in.