Machine learning of phase transitions in nonlinear polariton lattices

Abstract

Polaritonic lattices offer a unique testbed for studying nonlinear driven-dissipative physics. They show qualitative changes of a steady state as a function of system parameters, which resemble non-equilibrium phase transitions. Unlike their equilibrium counterparts, these transitions cannot be characterised by conventional statistical physics methods. Here, we study a lattice of square-arranged polariton condensates with nearest-neighbour coupling, and simulate the polarisation (pseudo-spin) dynamics of the polariton lattice, observing regions with distinct steady-state polarisation patterns. We classify these patterns using machine learning methods and determine the boundaries separating different regions. First, we use unsupervised data mining techniques to sketch the boundaries of phase transitions. We then apply learning by confusion, a neural network-based method for learning labels in the dataset, and extract the polaritonic phase diagram. Our work takes a step towards AI-enabled studies of polaritonic systems.

to appear in Communications Physics

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Cited by in corpus (9)

• A learning by confusion approach to characterize phase transitions
• Replacing neural networks by optimal analytical predictors for the detection of phase transitions
• Mapping out phase diagrams with generative classifiers
• Persistent homology of quantum entanglement
• Spin-polarized antichiral exciton-polariton edge states
• Learning entanglement breakdown as a phase transition by confusion
• Microscopic theory of nonlinear phase space filling in polaritonic lattices
• Learning by Confusion: The Phase Diagram of the Holstein Model
• Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer