Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials

Area of Science:

• Materials Science
• Condensed Matter Physics
• Data Science

Background:

• Defects are inherent in two-dimensional (2D) materials, significantly impacting their properties.
• Advanced transmission electron microscopy (TEM) generates large, complex datasets for defect analysis.
• Traditional manual analysis of TEM data is subjective, inefficient, and imprecise.

Purpose of the Study:

• To review recent advancements in analyzing defects in 2D materials using deep learning with TEM data.
• To highlight the transition from local characterization to intelligent macroscopic analysis enabled by big data.
• To explore the application of deep learning techniques for quantitative defect identification.

Main Methods:

• Utilizing deep learning algorithms for the quantitative identification of defects in 2D materials from TEM datasets.
• Examining the distinctions between TEM images and natural images for tailored data analysis.
• Applying deep learning for denoising, and identifying point, line, and planar defects.

Main Results:

• Deep learning offers efficient and precise methods for defect identification in 2D materials.
• This technology overcomes the limitations of traditional, localized TEM analysis.
• The review covers various aspects of TEM data analysis, including quantitative assessments and applications.

Conclusions:

• Deep learning transforms TEM data analysis for 2D material defects, enabling intelligent macroscopic insights.
• Accurate identification of distinct defect structures remains a significant challenge.
• Future research should focus on overcoming these obstacles for more robust defect characterization.