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Solving the Coloring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation.

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This study uses machine learning to predict atomic site preferences in half-Heusler compounds. The model accurately identifies correct site assignments, validated by experimental synthesis and calculations.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Crystallography

Background:

  • Half-Heusler compounds are an important class of materials with diverse applications.
  • Determining site preferences in crystal structures is crucial for understanding material properties.
  • Experimental characterization of site preferences can be complex and time-consuming.

Purpose of the Study:

  • To develop a machine-learning model for predicting site preferences in half-Heusler compounds.
  • To validate the model's predictions through experimental synthesis and characterization.
  • To demonstrate the utility of machine learning for data sanitization in materials science.

Main Methods:

  • A support-vector machine algorithm was employed.
  • The model was trained on 179 experimentally reported structures and 23 composition-based descriptors.
  • Machine learning predictions were validated using single-crystal and powder X-ray diffraction.
  • First-principles calculations were used to determine the lowest energy configurations.

Main Results:

  • The machine-learning model achieved high performance: 93% sensitivity, 96% selectivity, and 95% accuracy.
  • Two compounds, GdPtSb and HoPdBi, flagged for potential site assignment errors, were resynthesized and characterized.
  • Experimental and computational results confirmed the model's predicted site assignments.
  • The model effectively identified and corrected potentially erroneous site assignments in the training data.

Conclusions:

  • Machine learning provides an efficient and accurate method for predicting site preferences in half-Heusler compounds.
  • The developed model can aid in the discovery and refinement of crystal structures.
  • This approach enhances data quality and accelerates materials research by identifying and correcting structural inaccuracies.