  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.
