Quantitative Convolutional Neural Network Based Multi-Phase XRD Pattern Analysis

Quantitative Convolutional Neural Network Based Multi-Phase XRD Pattern Analysis

X-ray diffraction (XRD) is commonly used to analyze phase compositions of crystalline samples. Medical applications include the analysis of biotechnological materials and gall- and kidney stones, where composition can inform pathology assessment. XRD analysis methods like Rietveld refinement require...

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Bibliographic Details
Main Authors: Höfer Hawo H., Orth André, Schweidler Simon, Breitung Ben, Aghassi-Hagmann Jasmin, Reischl Markus
Format: Article
Language:English
Published: De Gruyter 2024-12-01
Series:Current Directions in Biomedical Engineering
Subjects:
xrd composition analysis
convolutional neural networks
deep learning
Online Access:https://doi.org/10.1515/cdbme-2024-2075
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Summary:X-ray diffraction (XRD) is commonly used to analyze phase compositions of crystalline samples. Medical applications include the analysis of biotechnological materials and gall- and kidney stones, where composition can inform pathology assessment. XRD analysis methods like Rietveld refinement requires expert knowledge, and multi-phase sample analysis is especially challenging and time consuming. Largescale medical and biotechnological experiments can therefore be hindered by the need to perform analysis tasks using XRD. Here, we present preliminary results on an automated convolutional neural network (CNN) based method for sample composition analysis using XRD patterns. It can aid experts’ analysis using initial estimations, and enable basic judgements for non-experts. Furthermore, we confirm the intuitive notion that analysis performance degrades with sample complexity through systematic investigation using a synthetic dataset.
ISSN:2364-5504

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