  Machine learning techniques can reveal hidden structure in large data amounts
and can potentially extent or even replace analytical scientific methods. In
nanophotonics, modes can increase the light yield from emitters located inside
the nanostructure or near the surface. Optimizing such systems enforces to
systematically analyze large amounts of three-dimensional field distribution
data. We present a method based on finite element simulations and machine
learning for the identification of modes with large field energies and specific
spatial properties. By clustering we reduce the field distribution data to a
minimal subset of prototypes. The predictive power of the approach is
demonstrated using an analysis of experimentally measured fluorescence
enhancement of quantum dots on a photonic crystal surface. The clustering
method can be used for any optimization task that depends on three-dimensional
field data, and is therefore relevant for biosensing, quantum dot solar cells
or photon upconversion.
