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Performance of a deep convolutional neural network to classify crystal structures using selected area electron beam

Jae Min Jeong1, Moonsoo Ra2, Jinha Jeong2

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A deep convolutional neural network (ResNet) shows potential in analyzing electron diffraction patterns for crystal defects. While accurate for lattice vibrations and strains, it struggles with dislocations and twin boundaries, requiring further training.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Machine Learning Applications

Background:

  • Selected Area Electron Diffraction Patterns (SADP) contain crucial information about crystal lattice defects.
  • Deep Convolutional Neural Networks (DCNNs) show promise in analyzing complex materials data.
  • Accurate defect identification is vital for understanding material properties.

Purpose of the Study:

  • To evaluate the performance of a ResNet architecture in classifying SADP datasets with various lattice defects.
  • To explore dataset augmentation strategies for improving DCNN performance in materials science.
  • To assess the capability of ResNet to identify strains, thermal vibrations, point defects, dislocations, and twin boundaries.

Main Methods:

  • Simulated SADP datasets incorporating lattice defects (strains, vibrations, point defects, dislocations, twin boundaries).
  • Utilized *ab initio* molecular dynamics, first principles geometry optimizations, and lattice manipulation for defect simulation.
  • Trained and tested a pre-trained ResNet model on simulated SADP datasets.

Main Results:

  • ResNet achieved acceptable classification accuracy for lattice vibrations and point defects, with performance decreasing as disorder increased.
  • The model demonstrated sensitivity to lattice symmetry changes under high strain levels.
  • ResNet failed to accurately recognize crystal structures affected by dislocations and twin boundaries.

Conclusions:

  • ResNet shows potential for defect analysis in SADPs but requires further development for comprehensive defect identification.
  • Future DCNN architectures should be trained on diverse defect scenarios to improve general classification performance.
  • Enhancing DCNNs to recognize subtle feature changes in diffraction patterns related to defects is crucial.