Papers › Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal...
Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-picometer Precision
Chia-Hao Lee, Abid Khan, Di Luo, Tatiane P. Santos, Chuqiao Shi, Blanka E. Janicek, Sangmin Kang, Wenjuan Zhu, Nahil A. Sobh, André Schleife, Bryan K. Clark, Pinshane Y. Huang
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2D materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so-far limited electron microscopy methods to probe these atomic-scale strain fields. Here, we demonstrate an approach to probe single-atom defects with sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe₂₋₂ₓTe₂ₓ. We utilize deep learning to mine large datasets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. By combining hundreds of images of nominally identical defects, we generate high signal-to-noise class-averages which allow us to measure 2D atomic coordinates with up to 0.3 pm precision. Our methods reveal that Se vacancies introduce complex, oscillating strain fields in the WSe₂₋₂ₓTe₂ₓ lattice which cannot be explained by continuum elastic theory. These results indicate the potential impact of computer vision for the development of high-precision electron microscopy methods for beam-sensitive materials.
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