"Advanced microscopy techniques applied to cutting edge material systems"
All branches of science need of techniques capable of measuring the relevant behaviors and comparing them to theoretical predictions in order to harness the phenomena at hand, and Materials Science is second to none in this respect. In the last couple of decades, in fact, we have witnessed dramatic advances in electron microscopy techniques capable of probing matter at the atomic scale in real space. However, more often than not, technical advances have come hand in hand with the production of large volumes of experimental data, which can be difficult to analyze and hamper progress. The main objective of this project is, precisely, to review the latest advances and achievements of artificial intelligence techniques applied to the quantification of scanning transmission electron microscopy (STEM) datasets. Special attention will be paid to applications to diffraction, multidimensional 4D-STEM images. These have become very popular in the last years because they allow mapping of electromagnetic fields in materials with atomic resolution, paving the way to new developments and materials systems of interest in fields as diverse as energy, spintronics or nanotechnology.