Machine Learning for EM

A toy model for the EEL spectrum and its derivative (in the inset)

Here we develop a machine learning method, inspired by particle physics techniques, to parametrize and subtract the zero-loss peak in electron energy-loss spectroscopy measurements. As an application, we determine the local bandgap of 2H/3R polytypic WS2.

Schematic representation of the output of EELSfitter with a 2D map of the bandgap energy extracted from WS2 nanostructures

Here we present a novel strategy based on machine learning techniques making possible the automated processing and interpretation of spectral images from electron energy loss spectroscopy for the determination of the bandgap and complex dielectric function in 2D materials achieving a spatial resolution down to a few nanometers.

Spatially-resolved bandgap and dielectric function in 2D materials from Electron Energy Loss Spectroscopy“, A. Brokkelkamp, J. ter Hoeve, I. Postmes, S. E. van Heijst, L. Maduro, A. V. Davydov, S. Krylyuk, J. Rojo, and S. Conesa Boj (2021), under review.