A machine learning system is particularly advantageous in a field like this, as you don't have to outline exactly what you're looking for. You can identify the exact conditions that prompt a transition without knowing what they are, and theoretically spot previously undiscovered transitions.

There's a lot of work to be done. It's easy to detect known transitions in a lab, where you can limit the number of particles, but it's much harder when you're looking at the overwhelming volume of particles in real life. If researchers can achieve that feat, though, they could discover reproducible behavior that might be useful in products, such as superconductors with more forgiving properties.