  Chiral photonics opens new pathways to manipulate light-matter interactions
and tailor the optical response of meta-surfaces and -materials by
nanostructuring nontrivial patterns. Chirality of matter, such as that of
molecules, and light, which in the simplest case is given by the handedness of
circular polarization, have attracted much attention for applications in
chemistry, nanophotonics and optical information processing. We report the
design of chiral photonic structures using two machine learning methods, the
evolutionary algorithm and neural network approach, for rapid and efficient
optimization of optical properties for dielectric metasurfaces. The design
recipes obtained for visible light in the range of transition-metal
dichalcogenide exciton resonances show a frequency-dependent modification in
the reflected light's degree of circular polarization, that is represented by
the difference between left- and right-circularly polarized intensity. Our
results suggest the facile fabrication and characterization of optical
nanopatterned reflectors for chirality-sensitive light-matter coupling
scenarios employing tungsten disulfide as possible active material with
features such as valley Hall effect and optical valley coherence.
