A transmission electron microscope micro-area diffraction crystal direction analysis method combined with a graph neural network

CN122453641APending Publication Date: 2026-07-24UNIV OF SCI & TECH BEIJING +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
UNIV OF SCI & TECH BEIJING
Filing Date
2026-04-29
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing methods for determining crystal orientation in micro-area diffraction during transmission electron microscopy rely on manual measurement, which is highly dependent on the operator's experience. Furthermore, when dealing with high-index weak spots, lattice distortions, or complex orientation relationships, it is difficult to balance calibration efficiency and accuracy. Existing graph structure learning methods have failed to effectively integrate crystallographic physical laws, resulting in significant errors.

Method used

By constructing a constrained topology graph and utilizing a graph neural network in combination with reciprocal space vectors and normalized intensity, the edge weights and topology construction parameters are dynamically adjusted, and the crystal orientation analysis is iteratively optimized, thereby improving the overall robustness of calibration for complex diffraction patterns.

Benefits of technology

It improves the overall calibration accuracy and robustness of complex diffraction patterns, enhances the ability to detect weak signal spots, reduces errors, and improves the interpretability and accuracy of crystal plane index analysis.

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Abstract

The present application relates to the technical field of electron microscope image analysis, and particularly relates to a transmission electron microscope micro-area diffraction crystal direction analysis method combined with a graph neural network, comprising: pre-processing a diffraction image, extracting a spot and converting it into a reciprocal space vector node; verifying the sum of any three node vectors and a deviation, establishing a connection edge if the deviation is within a threshold value, and constructing a constraint topological graph; inputting the constraint topological graph into a graph neural network, calculating edge weights and aggregating information according to neighbor node strength and reciprocal space vectors, and outputting each node crystal plane index prediction; reconstructing a theoretical reciprocal space vector based on the prediction result, calculating an overall error, and if the error is out of limit, iteratively adjusting parameters until a requirement is met or a maximum number of times is reached, and outputting a final result, and outputting a final crystal direction analysis result. The present application introduces crystallography priori through a constraint topological graph, combines with a graph neural network iterative optimization, and improves the crystal direction analysis accuracy and robustness of a complex diffraction pattern.
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