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Updated: Jul 30, 2025

$Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples$

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CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation.

Tom Kirstein¹, Lukas Petrich¹, Ravi Raj Purohit Purushottam Raj Purohit²

¹Institute of Stochastics, Ulm University, 89096 Ulm, Germany.

Materials (Basel, Switzerland)

|May 13, 2023

Summary

This study introduces a new method using convolutional neural networks to rapidly analyze Laue spots in X-ray microdiffraction, significantly speeding up crystallographic analysis and improving accuracy for materials science research.

Keywords:

Laue microdiffraction Laue spot morphology Laue spot quality convolutional neural network polycrystalline material runtime performance

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Area of Science:

Materials Science
Crystallography
Computational Science

Background:

Laue microdiffraction non-destructively maps crystallographic orientation and strain in materials.
Analysis of Laue patterns requires accurate characterization of thousands of Laue spots per spatial map.
Existing methods for spot analysis face computational challenges with large datasets and difficulties with irregular spots.

Purpose of the Study:

To develop a computationally efficient method for characterizing Laue spots.
To improve the accuracy of Laue spot analysis, especially for irregular spots.
To accelerate the processing of large Laue microdiffraction datasets.

Main Methods:

Review of existing Laue spot characterization methods (image moments, function fitting).
Development and implementation of a convolutional neural network (CNN) approach.
Testing the CNN on unseen Laue spot data using GPU acceleration.

Main Results:

The CNN approach significantly accelerates Laue spot characterization.
The method maintains high accuracy comparable to traditional methods.
Achieved a 77x acceleration using GPU for unseen Laue spots.

Conclusions:

Convolutional neural networks offer a powerful solution for accelerating Laue microdiffraction data analysis.
The presented method effectively handles large datasets and improves the characterization of complex Laue spots.
This advancement facilitates more efficient and accurate crystallographic and strain analysis in materials science.