ArticleSmall science2023
A Multiscale Deep-Learning Model for Atom Identification from Low-Signal-to-Noise-Ratio Transmission Electron Microscopy Images.
Article in Small science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine Vision-Enabled Octahedral Network Reconstruction and Structural Analysis of Perovskite Quantum Dots.ACS nano · 2026Article
- STEMDiff: A Wavelet-Enhanced Diffusion Model for Physics-Informed STEM Image Generation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Auto-resolving the atomic structure at van der Waals interfaces using a generative model.Nature communications · 2025Article
- Automated image acquisition and analysis of graphene and hexagonal boron nitride from pristine to highly defective and amorphous structures.Scientific reports · 2024Article
- Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials.iScience · 2023Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in transmission electron microscopy (TEM) have enabled the study of atomic structures of materials at unprecedented scales as small as tens of picometers (pm). However, accurately detecting atomic positions from TEM images remains a challenging task. Traditional Gaussian fitting and peak-finding algorithms are effective under ideal conditions but perform poorly on images with strong background noise or contamination areas (shown as ultrabright or ultradark contrasts). Moreover, these traditional algorithms require parameter tuning for different magnifications. To overcome these challenges, AtomID-Net is presented, a deep neural network model for atomic detection from multiscale low-SNR experimental images of scanning TEM (scanning transmission electron microscopy (STEM)). The model is trained on real images, which allows the robust and efficient detection of atomic positions, even in the presence of background noise and contamination. The evaluation on a test set of 50 images with a resolution of 800 × 800 yields an average F1-Score of 0.964, which demonstrates significant improvements over existing peak-finding algorithms.
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