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.
O.K. thanks Oleksandr Balabanov, Bart Olsthoorn and Stanislav Borysov for useful suggestions. I.A.S. and I.I. acknowledge the support from Russian Foundation for Basic Research (RFBR), in framework of the joint RFBR-DFG project No. 21-52-12038. O.K. acknowledges the support from UK EPSRC New Investigator Award under the Agreement No. EP/V00171X/1, as well as NATO Science for Peace and Security project NATO.SPS.MYP.G5860. H.S. acknowledges the support of the Icelandic Research Fund (Rannis), grant no. 217631-051. V.K.K and I.A.S. acknowledge support from Icelandic Research Fund (Rannis), grant no. 163082-051.
© 2022, The Author(s).