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The electronic properties of two-dimensional (2D) materials dependsensitively on the underlying atomic arrangement down to the monolayer level.Here we present a novel strategy for the determination of the band gap andcomplex dielectric function in 2D materials achieving a spatial resolution downto a few nanometers. This approach is based on machine learning techniquesdeveloped in particle physics and makes possible the automated processing andinterpretation of spectral images from electron energy-loss spectroscopy(EELS). Individual spectra are classified as a function of the thickness with$K$-means clustering and then used to train a deep-learning model of thezero-loss peak background. As a proof-of-concept we assess the band gap anddielectric function of InSe flakes and polytypic WS$_2$ nanoflowers, andcorrelate these electrical properties with the local thickness. Our flexibleapproach is generalizable to other nanostructured materials and tohigher-dimensional spectroscopies, and is made available as a new release ofthe open-source EELSfitter framework.

Spatially-Resolved Band Gap and Dielectric Function in 2D Materials from Electron Energy Loss Spectroscopy