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2D materials offer an ideal platform to study the strain fields induced byindividual atomic defects, yet challenges associated with radiation damage haveso-far limited electron microscopy methods to probe these atomic-scale strainfields. Here, we demonstrate an approach to probe single-atom defects withsub-picometer precision in a monolayer 2D transition metal dichalcogenide,WSe$_{2-2x}$Te$_{2x}$. We utilize deep learning to mine large datasets ofaberration-corrected scanning transmission electron microscopy images to locateand classify point defects. By combining hundreds of images of nominallyidentical defects, we generate high signal-to-noise class-averages which allowus to measure 2D atomic coordinates with up to 0.3 pm precision. Our methodsreveal that Se vacancies introduce complex, oscillating strain fields in theWSe$_{2-2x}$Te$_{2x}$ lattice which cannot be explained by continuum elastictheory. These results indicate the potential impact of computer vision for thedevelopment of high-precision electron microscopy methods for beam-sensitivematerials.

Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-picometer Precision