Spectral barcoding of quantum dots: Deciphering structural motifs from the excitonic spectra

Self-assembled semiconductor quantum dots (QDs) show in high-resolution single-dot spectra a multitude of sharp lines, resembling a barcode, due to various neutral and charged exciton complexes. Here we propose the ``spectral barcoding'' method that deciphers structural motifs of dots by using such barcode as input to an artificial-intelligence learning system. Thus, we invert the common practice of deducing spectra from structure by deducing structure from spectra. This approach (i) lays the foundation for building a much needed structure-spectra understanding for large nanostructures and (ii) can guide future design of desired optical features of QDs by controlling during growth only those structural motifs that decide given optical features.