Between Consenting Electrons

Electrons are notoriously fickle things, orbiting one proton, then another, their paths described in terms of probability. Scientists can observe their travel indirectly using scanning tunneling microscopes, but the flood of data from these instruments — tracking up to a trillion trillion particles —  is a challenge to interpret. Neural nets may offer a better way.

What's new: Physicists at Cornell University developed a neural network capable of finding patterns in electron microscope images. They began by training the model on simulated images of electrons passing through an idealized environment. Once it learned to associate certain electron behaviors with theories that explain them, the researchers set it on real world data from electrons interacting with certain materials. The network successfully detected subtle features of the electrons’ behavior.

Results: The researchers were trying to deduce whether electrons traveling through high-temperature superconductors were driven more by kinetic energies or repulsion among electrons. Their conclusions, published in Nature, confirmed that the electrons passing through these materials were influenced most by repulsive forces.

What they’re saying: "Some of those images were taken on materials that have been deemed important and mysterious for two decades. You wonder what kinds of secrets are buried in those images. We would like to unlock those secrets," said Eun-Ah Kim, Cornell University professor of physics and lead author of the study.

Why it matters: Technological progress often relies on understanding how electrons behave when they pass through materials — think of superconductors, semiconductors, and insulators.

Takeaway: Smarter computers need faster processors, and that depends on advances in material science. Understanding the forces that dominate an electron’s behavior within a given medium will allow scientists to develop high-performance computers that push the frontiers of AI.