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

Mie Sensing with Neural Networks: Recognition of Nano-Object Parameters, the Invisibility Point, and Restricted Models