A Multiscale Deep‐Learning Model for Atom Identification from Low‐Signal‐to‐Noise‐Ratio Transmission Electron Microscopy Images

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.

Keywords

• atomic positions
• image segmentations
• supervised learning
• transition metal dichalcogenides
• U-Net