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Machine learning accurately predicts semiconductor band alignment for oxides using bulk and surface data. This approach accelerates the understanding and screening of materials for electronic devices.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Band alignment of semiconductors, insulators, and dielectrics is crucial for device performance.
  • Ionization potential and electron affinity determine surface-dependent band-edge positions.
  • Accurate determination requires complex experiments or simulations.

Purpose of the Study:

  • To develop a machine learning model for predicting band alignment in nonmetallic oxides.
  • To enable rapid and systematic prediction of band positions for various oxide surfaces.

Main Methods:

  • Utilized a high-throughput first-principles calculation dataset of ~3000 oxide surfaces.
  • Developed a neural network model trained on bulk structure and surface termination information.
  • Extended the model to incorporate multiple-cation effects and apply to ternary oxides.

Main Results:

  • The neural network accurately predicts band positions for relaxed binary oxide surfaces.
  • The model effectively handles multiple-cation effects and transfers to ternary oxides.
  • Achieved accurate predictions using only bulk structure and surface termination data.

Conclusions:

  • Machine learning offers an efficient method for determining band alignment in nonmetallic oxides.
  • This approach facilitates systematic understanding and materials screening for electronic applications.
  • Enables prediction of band alignment for a vast range of solid surfaces.