  We study phase transitions in a lattice of square-arranged driven-dissipative
polariton condensates with nearest-neighbour coupling. Simulating the
polarization (spin) dynamics of the polariton lattice, we observe regions of
qualitatively different steady-state behaviour which can be identified in
time-integrated measurements. The transition between these regions resemble
phase transitions ubiquitous in statistical physics, but have inherently
non-equilibrium nature and cannot be classified in the conventional way. To
overcome this challenge, we use machine learning methods to determine the
boundaries separating the regions. We use unsupervised data mining techniques
to sketch the regions of phase transition. 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.
