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Exploring supervised machine learning for multi-phase identification and quantification from powder X-ray diffraction spectra

  • Computation & theory
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Abstract

Powder X-ray diffraction analysis is a critical component of materials characterization methodologies. Discerning characteristic Bragg intensity peaks and assigning them to known crystalline phases is the first qualitative step of evaluating diffraction spectra. Subsequent to phase identification, Rietveld refinement may be employed to extract the abundance of quantitative, material-specific parameters hidden within powder data. These characterization procedures are yet time-consuming and inhibit efficiency in materials science workflows. The ever-increasing popularity and propulsion of data science techniques has provided an obvious solution on the course toward materials analysis automation. Deep learning has become a prime focus for predicting crystallographic parameters and features from X-ray spectra. However, the infeasibility of curating large, well-labeled experimental datasets means that one must resort to a large number of theoretic simulations for powder data augmentation to effectively train deep models. Herein, we are interested in conventional supervised learning algorithms in lieu of deep learning for multi-label crystalline phase identification and quantitative phase analysis for a biomedical application. First, models were trained using very limited experimental data. Further, we incorporated simulated XRD data to assess model generalizability as well as the efficacy of simulation-based training for predictive analysis in a real-world X-ray diffraction application.

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Authors and Affiliations

  1. Department of Physics, The University of the West Indies, St. Augustine, Trinidad

    Jaimie Greasley

  2. Department of Computer Science, The University of the West Indies, St. Augustine, Trinidad

    Patrick Hosein

Authors
  1. Jaimie Greasley
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  2. Patrick Hosein
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Correspondence to Jaimie Greasley.

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The investigation of kidney stones was approved by the University of the West Indies St. Augustine Campus Research Ethics Committee.

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Handling Editor: Ghanshyam Pilania.

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Greasley, J., Hosein, P. Exploring supervised machine learning for multi-phase identification and quantification from powder X-ray diffraction spectra. J Mater Sci 58, 5334–5348 (2023). https://doi.org/10.1007/s10853-023-08343-4

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  • DOI: https://doi.org/10.1007/s10853-023-08343-4

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