Seeing an elusive magnetic effect through the lens of machine learning

Superconductors have long been considered the principal approach for realizing electronics without resistivity. In the past decade, a new family of quantum materials, “topological materials,” has offered an alternative but promising means for achieving electronics without energy dissipation (or loss). Compared to superconductors, topological materials provide a few advantages, such as robustness against disturbances. To attain the dissipationless electronic states, one key route is the so-called “magnetic proximity effect,” which occurs when magnetism penetrates slightly into the surface of a topological material. However, observing the proximity effect has been challenging.