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Using Similarity Metrics to Quantify Differences in High-Throughput Data Sets: Application to X-ray Diffraction

Efraín Hernández-Rivera1, Shawn P Coleman1, Mark A Tschopp1

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Summary
This summary is machine-generated.

This study quantifies differences in X-ray diffraction (XRD) patterns using 49 similarity metrics. The Clark metric proved most sensitive to peak changes, aiding structural analysis of virtual XRD patterns.

Keywords:
GaussianX-ray diffraction patternshigh-throughput datasetssensitivesimilarity metrics

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Area of Science:

  • Materials Science
  • Crystallography
  • Data Analysis

Background:

  • X-ray diffraction (XRD) patterns contain rich information about material structures.
  • Quantifying subtle differences in XRD patterns is crucial for understanding material properties and changes.
  • Existing methods may not fully capture the nuances of peak variations in diffraction data.

Purpose of the Study:

  • To demonstrate the utility of similarity metrics for quantifying differences in sets of diffraction patterns.
  • To evaluate the sensitivity of various similarity metrics to specific features in Gaussian-based peak responses, simulating XRD characteristics.
  • To propose a framework for analyzing structural alterations using virtual XRD patterns.

Main Methods:

  • Implementation of 49 distinct similarity metrics.
  • Analysis of Gaussian-based peak responses as surrogates for XRD patterns.
  • Application of hierarchical clustering analysis to assess metric behavior.
  • Generation of virtual XRD patterns to model structural changes.

Main Results:

  • Most similarity metrics exhibited unrelated responses during hierarchical clustering.
  • The Clark metric was identified as consistently sensitive to synthetic single peak variations.
  • A framework was established for analyzing structural changes like size convergence and isotropic straining.

Conclusions:

  • Similarity metrics offer a quantitative approach to differentiate sets of diffraction patterns.
  • The Clark metric shows promise for detecting subtle changes in XRD data.
  • The proposed framework facilitates the analysis of structural modifications through virtual XRD pattern analysis.