Detecting composite orders in layered models via machine learning

Understand

Determining the phase diagram of systems consisting of smaller subsystems 'connected' via a tunable coupling is a challenging task relevant for a variety of physical settings.

• A general question is whether new phases, not present in the uncoupled limit, may arise.
• We use machine learning to study layered spin models, in which the spin variables constituting each of the uncoupled systems (to which we refer as layers) are coupled to each other via an interlayer coupling.
• In such systems, in general, composite order parameters involving spins of different layers may emerge as a consequence of the interlayer coupling.