A Deep Learning Approach to Identify Local Structures in Atomic‐Resolution Transmission Electron Microscopy Images

By Jacob Madsen, Pei Liu, J. Kling, J. Wagner, T. Hansen, Ole Winther, J. Schiøtz. Advanced Theory and Simulations, 2018

185 citations recorded by the source corpus.

Recording atomic‐resolution transmission electron microscopy (TEM) images is becoming increasingly routine. A new bottleneck is then analyzing this information, which often involves time‐consuming manual structural identification. A deep learning‐based algorithm for recognition of the local structure in TEM images was developed, which is stable to microscope parameters and noise. The neural network is trained entirely from simulation but is capable of making reliable predictions on experimental images. The method is applied to single sheets of defected graphene, and to metallic nanoparticles on an oxide support.

Subfields: Atomic and Molecular Physics, and Optics, Structural Biology, Surfaces, Coatings and Films

View Paper: https://www.semanticscholar.org/paper/5d89617fee5d916a0ac389c73ef8311b7f713fd9

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In Atomic and Molecular Physics, and Optics

1. Chi Chen
2. Chris Wolverton
3. Simon J. L. Billinge
4. Shyue Ping Ong

Citations in the corpus

• Recent advances and applications of deep learning methods in materials science (2021, methodology): “applied pixelwise DL to detect atoms in simulated atomicresolution TEM images of graphene [274].”
• Optical wafer defect inspection at the 10 nm technology node and beyond (2022, background): “Transmission electronmicroscope (TEM) can resolve atomic structures [15, 16], but it requires a vacuum environment and cross-section sampling; thus, it is only widely used in the semiconductor industry as a reference tool to label data.”