![]() Temperature measurement by a nanoscale electron probe using energy gain and loss spectroscopy. Vibrational spectroscopy in the electron microscope. Breaking the Crowther limit: combining depth-sectioning and tilt tomography for high-resolution, wide-field 3D reconstructions. Combined scanning transmission electron microscopy tilt- and focal series. Depth sectioning combined with atom-counting in HAADF STEM to retrieve the 3D atomic structure. Nanomaterial datasets to advance tomography in scanning transmission electron microscopy. Single atom visibility in STEM optical depth sectioning. Bright-field scanning confocal electron microscopy using a double aberration-corrected transmission electron microscope. Optical sectioning and confocal imaging and analysis in the transmission electron microscope. Depth sectioning with the aberration-corrected scanning transmission electron microscope. Three-dimensional imaging of individual hafnium atoms inside a semiconductor device. Aberration-corrected ADF-STEM depth sectioning and prospects for reliable 3D imaging in S/TEM. Three-dimensional imaging of individual dopant atoms in SrTiO 3. Resolution beyond the information limit in transmission electron-microscopy. Atomic electric fields revealed by a quantum mechanical approach to electron picodiffraction. Disentangling nanoscale electric and magnetic fields by time-reversal operation in differential phase-contrast STEM. Introduction to Scanning Transmission Electron Microscopy (Routledge, 2018). ![]() Finally, we discuss the application of ML to automating experiments and novel scanning modes. We present the critical infrastructural needs for the broad adoption of ML methods in the STEM community, including the storage of data and metadata to allow the reproduction of experiments. We show examples of the opportunities offered by structural STEM imaging in elucidating the chemistry and physics of complex materials and how the latter connect to first-principles and phase-field models to yield consistent interpretation of generative physics. We discuss the quantification of STEM structural data as a necessary step towards meaningful ML applications and its analysis in terms of the relevant physics and chemistry. We review the primary STEM imaging methods, including structural imaging, electron energy loss spectroscopy and its momentum-resolved modalities and 4D-STEM. This Primer focuses on the opportunities emerging at the interface between STEM and machine learning (ML) methods. Driven by advances in aberration correction, STEM now allows the routine imaging of structures with single-digit picometre-level precision for localization of atomic units. Scanning transmission electron microscopy (STEM) has emerged as a uniquely powerful tool for structural and functional imaging of materials on the atomic level.
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