This page offers DL-driven tools for analyzing powder XRD. With this tool, users can accurately predict X-ray diffraction (XRD) results for 7 crystal systems, 101 extinction groups, and 230 space groups. In addition, a GAN(pix2pix) is used to process single-phase powder XRD and produce high-quality results. This package simplifies crystallographic analysis for materials scientists.
Users can download 197,131 standard XRD Patterns, perturbed XRD patterns, and texture-perturbed XRD patterns from the ./XRD_analysis.ipynb notebook file.
The code is tested in under packages.
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OS : Ubuntu 22.04
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python : 3.9.13
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Tensorflow : 2.8.1
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numpy : 1.23.1
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FullProf : July-2017
All the codes are available as Jupyter Notebook files
./XRD_analysis.ipynb.
./XRD_Generator.ipynb.
Please cite follwing
Lee et al. A Deep Learning Approach to Powder XRD Pattern Analysis: Addressing Generalizability and Perturbation Issues Simultaneously J. Rodriguez-Carvajal, Recent Developments of the Program FULLPROF, in Commission on Powder Diffraction (IUCr). Physica B.(1993), 192, 55
DOI and link will be updated.
This software is released under the MIT License.