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Related Experiment Video

Updated: Jun 14, 2025

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

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

Published on: June 19, 2018

AI-enhanced X-ray diffraction analysis: towards real-time mineral phase identification and quantification.

Nikolaos I Prasianakis¹

¹Laboratory for Waste Management, Paul Scherrer Institute, Forschungsstrasse 111, Villigen PSI, 5232 Switzerland.

|August 30, 2024

Summary

Convolutional neural networks can revolutionize X-ray diffraction (XRD) analysis by significantly reducing processing times. This study provides an initial assessment of the accuracy of these methods using synthetic and real mineral data.

Keywords:

computational modeling convolutional neural networks mineral phase identification powder X-ray diffraction

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Published on: May 10, 2021

Area of Science:

Materials Science
Computational Chemistry
Geology

Background:

X-ray diffraction (XRD) is a crucial technique for mineral identification and quantification.
Traditional XRD analysis can be time-consuming, limiting its application in high-throughput scenarios.

Purpose of the Study:

To investigate the potential of convolutional neural networks (CNNs) to accelerate XRD analysis.
To evaluate the accuracy of CNN-based XRD analysis compared to conventional methods.

Main Methods:

Development and application of CNN models for XRD pattern processing.
Testing the models on both synthetic and real-world mineral mixture datasets.

Main Results:

CNNs significantly reduce the processing time required for XRD analysis.
The developed methods demonstrate a promising level of accuracy in analyzing mineral mixtures.

Conclusions:

Convolutional neural networks offer a revolutionary approach to XRD analysis, enhancing speed and efficiency.
Further research is warranted to fully establish the capabilities and limitations of CNNs in quantitative XRD analysis.