• UW DIRECT project of a general XRD image pattern classifier framework for predicting crystal structure.

Table of Contents

• Background
• Purpose
• Repository Structure
• How to install
• Examples and Demos
• How to use our software
• License

Background

All crystalline materials in nature have chemical and physical properties strongly dependent on atomic structures

• Single-crystal X-ray diffraction
  • Limitations: nanosized crystals, intergrowth, and defects
• Powder X-ray diffraction
  • Limitations: complex structures, multiphasic samples, impurities, peak overlaps
• Alternatives: electron crystallography (EC), high-resolution transmission electron microscopy (HRTEM)
  • Requires considerable expertise on the operation of electron microscopes and crystallography

SOLUTION: to use CNN to predict crystal structure from any XRD pattern

Purpose

This project will aim to use CNN to predict the crystal structure of a sample based upon its XRD pattern. In all cases, the users will inputting the XRD pattern that they obtained into our XRayDawgz, which will return a predicted crystal structure.

Repository Structure

  |- README.md
  |- docs/
      |- Tech_review.pdf
      |- functional_spec.md
      |- component_spec.md
      |- UW Final presentation.pdf
  |- icon/
      |- banner.png
      |- icon.png
  |- xraydawgz/
      |- __init__.py
      |- Core.py
      |- version.py
      |- Predict.py
      |- crystal_structure_classifier.h5      
      |- XRD_Images/
        |- Test/
          |- BCC_test/
          |- FCC_test/
        |- Train/
          |- BCC_train/
          |- FCC_train/
        |- demo_images/
          |- BCC/
          |- FCC/
      |- Demo/
        |- Demo_FCC_prediction.ipynb
        |- demo.PNG
      |- tests/
        |- __init__.py
        |- test_core.py
        |- Images/
        |- cut_image/
          |- Test/
          |- Train/
  |- setup.py
  |- .travis.yml
  |- .coveragerc
  |- .coverage.yml
  |- environment.yml
  |- .gitignore
  |- LICENSE 
  |- requirements.txt

How to install

1. Install tensorflow and keras

   conda install tensorflow

   conda install keras

2. Use the following method to clone repo

   • press the green botton at the home page of our repo, and choose "Download Zip".

   • unzip the file and you would get the file name "XRayDawgz" in your path.

Examples and Demos

The example below shows a user creating a jupyter notebook in the directory xraydawgz after cloning this repository. The user called the function with an input containing their path_to_folder with the images they would like to test.

How to use our software

1. Prepare the image of XRD pattern and place the image(s) in a folder.

2. In the XRayDawgz/xraydawgz, the user will:

   import Predict

3. User will call Predict.crystal_structure("path_to_the_file_including_images"), you can readily check the path by dragging the file into the terminal!

   predict.crystal_structure("path_to_the_file_included_image")

License