  In this work, we use artificial neural networks (ANNs) to recognize the
material composition, sizes of nanoparticles and their concentrations in
different media with high accuracy, solely from the absorbance spectrum of a
macroscopic sample. We construct ANNs operating in the following two schemes.
The first scheme is designed to recognize the dimensions and refractive indices
of dielectric scatterers in mixed ensembles. The second ANN model
simultaneously recognizes the dimensions of gold nanospheres in a mixture and
the refractive index of a matrix. A challenge in the first scheme arises at and
near the invisibility point, i.e., when the refractive index of nanoparticles
is close to that of the medium. Of course, particle recognition in this regime
faces fundamental physical limitations. However, such recognition near the
invisibility point is possible, and our study reveals its unique properties.
Interestingly, the recognition process for the refractive index in the vicinity
of the invisibility point shows very small errors. In contrast, the errors for
the recognition of the radius grow strongly near this point. Another regime
with limited recognition occurs when the extinction spectra are not unique and
can correspond to different realizations of nanoparticle mixtures. Regarding
multi-particle or polydisperse solutions, the ML-based models should in such
cases be rationally restricted to maintain the feasibility of the recognition
process. Overall, the recognition schemes proposed and investigated by us can
find their applications in the field of sensing.
