Artifact identification in X-ray diffraction data using machine learning methods.

Area of Science:

• Materials Science
• Crystallography
• Data Science

Background:

• In situ synchrotron high-energy X-ray powder diffraction (XRD) is crucial for analyzing material structures in functional devices and complex environments.
• Rietveld refinement of XRD patterns provides detailed information on crystallographic structure, size, strain, and defects.
• Realistic XRD images often contain artifacts like preferred orientations and single-crystal diffraction spots, complicating analysis.

Purpose of the Study:

• To investigate machine learning methods for the fast and reliable identification and separation of single-crystal diffraction spots in XRD images.
• To improve the accuracy of crystallographic analysis by excluding artifacts during image integration.
• To reduce the time-consuming nature of manual identification and separation of these diffraction spots.

Main Methods:

• Development and application of machine learning algorithms, specifically gradient boosting, for analyzing XRD images.
• Training machine learning models with diverse datasets to ensure high accuracy and generalization.
• Implementing artifact exclusion during the XRD image integration process.

Main Results:

• The gradient boosting method demonstrated high accuracy in identifying and separating single-crystal diffraction spots.
• The machine learning approach significantly decreased the time required for artifact removal compared to conventional methods.
• Accurate exclusion of artifacts enabled more precise analysis of powder diffraction rings.

Conclusions:

• Machine learning, particularly gradient boosting, offers a powerful tool for automated analysis of in situ XRD data.
• This method enhances the efficiency and reliability of crystallographic structure determination from complex XRD patterns.
• The findings facilitate advanced materials research by streamlining the analysis of challenging experimental data.