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Yu Meng(蒙宇), Shuya Wang(王淑雅), Xibiao Ren(任锡标), Han Xue(薛涵), Xuejun Yue(岳学军), Chuanhong Jin(金传洪), Shanggang Lin(林上港), and Fang Lin(林芳). 2025: Combining machine learning algorithms with traditional methods for resolving the atomic-scale dynamic structure of monolayer MoS2 in high-resolution transmission electron microscopy, Chinese Physics B, 34(1): 016802. doi: 10.1088/1674-1056/ad9ba3
Citation: Yu Meng(蒙宇), Shuya Wang(王淑雅), Xibiao Ren(任锡标), Han Xue(薛涵), Xuejun Yue(岳学军), Chuanhong Jin(金传洪), Shanggang Lin(林上港), and Fang Lin(林芳). 2025: Combining machine learning algorithms with traditional methods for resolving the atomic-scale dynamic structure of monolayer MoS2 in high-resolution transmission electron microscopy, Chinese Physics B, 34(1): 016802. doi: 10.1088/1674-1056/ad9ba3
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Combining machine learning algorithms with traditional methods for resolving the atomic-scale dynamic structure of monolayer MoS2 in high-resolution transmission electron microscopy

  • 1 Department of Applied Physics, College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China;

  • 2 State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China;

  • 3 Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

  • Received Date: 01/10/2024
    Accepted Date: 01/11/2024
    Available Online: 20/01/2025
  • Fund Project:

    F. Lin acknowledges financial support from the National Natural Science Foundation of China (Grant No. 61971201).

  • High-resolution transmission electron microscopy (HRTEM) promises rapid atomic-scale dynamic structure imaging. Yet, the precision limitations of aberration parameters and the challenge of eliminating aberrations in $Cs$-corrected transmission electron microscopy constrain resolution. A machine learning algorithm is developed to determine the aberration parameters with higher precision from small, lattice-periodic crystal images. The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide (MoS$_{2}$) monolayer with 25 variables (14 aberrations) resolved in wide ranges. Using these measured parameters, the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS$_{2}$ monolayers. The images were acquired due to the unexpected movement of the specimen holder. Four-dimensional data extraction reveals time-varying atomic structures and ripple. In particular, the atomic evolution of the sulfur-vacancy point and line defects, as well as the edge structure near the amorphous, is visualized as the resolution has been improved from about 1.75 Å to 0.9 Å. This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.

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Combining machine learning algorithms with traditional methods for resolving the atomic-scale dynamic structure of monolayer MoS2 in high-resolution transmission electron microscopy

  • Received Date: 01/10/2024
  • Accepted Date: 01/11/2024
  • Available Online: 20/01/2025
Fund Project: 

Abstract: 

High-resolution transmission electron microscopy (HRTEM) promises rapid atomic-scale dynamic structure imaging. Yet, the precision limitations of aberration parameters and the challenge of eliminating aberrations in $Cs$-corrected transmission electron microscopy constrain resolution. A machine learning algorithm is developed to determine the aberration parameters with higher precision from small, lattice-periodic crystal images. The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide (MoS$_{2}$) monolayer with 25 variables (14 aberrations) resolved in wide ranges. Using these measured parameters, the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS$_{2}$ monolayers. The images were acquired due to the unexpected movement of the specimen holder. Four-dimensional data extraction reveals time-varying atomic structures and ripple. In particular, the atomic evolution of the sulfur-vacancy point and line defects, as well as the edge structure near the amorphous, is visualized as the resolution has been improved from about 1.75 Å to 0.9 Å. This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.

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