Machine learning and neural networks recognize exotic insulating phases in quantum materials

Does it conduct electricity? Or insulate against electricity? Physicists commonly classify material phases as one or the other. Machine learning is a powerful tool for pattern recognition and thus could help identify phases of matter. However, machine learning needs a bridge to the quantum world, where the physics of atoms, electrons, and particles differs from that of larger objects or galaxies. Now, scientists have provided a bridge, which they call the quantum loop topography technique. This is a machine-learning algorithm based on neural networks. It detects with high efficiency an exotic phase where electricity is conducted around the material's surface but not through the middle. Also, it distinguishes between normal insulators and these exotic topological insulators.