Seyfal Sultanov

Abstract Atomic-scale defects govern many functional properties of materials, yet their systematic identification and quantification remain challenging because supervised learning approaches require extensive labeled datasets, which are scarce in atomic-resolution microscopy due to the complexity and diversity of defect structures.

1 Introduction High-resolution transmission electron microscopy has emerged as the predominant technique for material characterization across diverse systems, including 2D materials,[1]superconductors,[2] semiconductors,[3] and catalysts.[4] A particularly powerful approach to materials characterization is the combination of scanning transmission electron microscopy (STEM)[5] with electron energy-loss spectroscopy (EELS),[6] which can measure the local density of states up to single...