Machine learning of phase transitions in nonlinear polariton lattices

This is the final version. Available from Nature Research via the DOI in this record. The data that support the findings of this study are available from the corresponding author upon reasonable request.The code for the analysis is available from the corresponding author upon reasonable request.Polaritonic lattices offer a unique testbed for studying nonlinear driven-dissipative physics. They show qualitative changes of their 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 (pseudospin) 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 a dataset, and extract the polaritonic phase diagram. Our work takes a step towards AI-enabled studies of polaritonic systems.Engineering and Physical Sciences Research Council (EPSRC)Russian Foundation for Basic ResearchNATOIcelandic Research FundIcelandic Research Fun