Machine Learning-Enabled Fast-Scan STEM for the Precise Identification of Labile Single-Atom Sites

supports, circumventing the limitations of high-noise imaging. Analysis reveals random displacements of ∼3.2% relative to ideal lattice sites, offering a direct glimpse into intrinsic structural disorder. Furthermore, we observe persistent heterogeneity in the local electric field across Pt atomic columns in differential phase contrast (DPC) imaging. These results suggest that single-atom sites locate in a highly dynamic, nonideal equilibrium state. This study also establishes a rigorous computational framework for resolving atomic-scale structures under noisy imaging conditions.

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Publication Details

Journal
Nano Letters
Published
2026-05-17
DOI
https://doi.org/10.1021/acs.nanolett.6c01472
Primary Topic
Advanced Electron Microscopy Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Machine Learning-Enabled Fast-Scan STEM for the Precise Identification of Labile Single-Atom Sites

Yi Ding, Jiuhui Han, Hailong Zhang, Ruirui Liu et al.
Nano Letters
Advanced Electron Microscopy Techniques and Applications
article

Machine Learning-Enabled Fast-Scan STEM for the Precise Identification of Labile Single-Atom Sites

Yi Ding, Jiuhui Han, Hailong Zhang, Ruirui Liu, Ruining Jiang, Lu Yu, Chao Li
article en

Abstract

supports, circumventing the limitations of high-noise imaging. Analysis reveals random displacements of ∼3.2% relative to ideal lattice sites, offering a direct glimpse into intrinsic structural disorder. Furthermore, we observe persistent heterogeneity in the local electric field across Pt atomic columns in differential phase contrast (DPC) imaging. These results suggest that single-atom sites locate in a highly dynamic, nonideal equilibrium state. This study also establishes a rigorous computational framework for resolving atomic-scale structures under noisy imaging conditions.

Nano Letters
Tianjin University of Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 6%
Advanced Electron Microscopy Techniques and Applications
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