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Automated classification of big X-ray diffraction data using deep learning models

Engineering and Technology

Automated classification of big X-ray diffraction data using deep learning models

J. E. Salgado, S. Lerman, et al.

Unlock the potential of your X-ray diffraction data with a groundbreaking deep learning model developed by Jerardo E. Salgado and colleagues. This innovative approach overcomes the hurdles of traditional analysis, offering unparalleled accuracy in classifying crystal systems and space groups while adapting seamlessly to diverse experimental conditions.

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Playback language: English
Abstract
This paper introduces a generalized deep learning model for classifying materials' crystal systems and space groups using X-ray diffraction (XRD) data. The model addresses the limitations of current automated XRD analysis, which often lack performance and adaptability. The approach uses a holistically represented training dataset accounting for various experimental conditions and crystal properties, incorporates an expedited learning technique, and optimizes model architecture based on Bragg's Law. Evaluation on experimental data and unseen materials demonstrates state-of-the-art performance, particularly in space group classification.
Publisher
npj Computational Materials
Published On
Dec 04, 2023
Authors
Jerardo E. Salgado, Samuel Lerman, Zhaotong Du, Chenliang Xu, Niaz Abdolrahim
Tags
deep learning
X-ray diffraction
crystal systems
space groups
automated analysis
Bragg's Law
machine learning
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