Pablo Calvo-Barlés

Abstract Nano-optics aims to understand and control the propagation of light at the nanoscale through the excitation of surface polaritons: hybrid light–matter quasiparticles. Recently, twisted van der Waals materials have enabled unprecedented phonon polariton propagations, such as canalization. However, nano-optics still presents an important limitation: obtaining polariton propagations on demand.

We introduce a machine-learning approach (denoted symmetry seeker neural network) capable of automatically discovering discrete symmetry groups in physical systems. This method identifies the finite set of parameter transformations that preserve the system's physical properties. Remarkably, the method accomplishes this without prior knowledge of the system's symmetry or the mathematical relationships between parameters and properties.