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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Physical Metallurgy

Background:

  • Solute atom segregation at grain boundaries (GBs) significantly influences polycrystal properties.
  • Understanding solute segregation across diverse alloys and GB microenvironments is limited by the lack of atomistic simulation tools.

Purpose of the Study:

  • To develop an algorithmic framework for predicting solute segregation energies in metal polycrystals from ab initio methods.
  • To bypass the requirement for alloy-specific interatomic potentials in atomistic simulations.

Main Methods:

  • Development of a novel algorithmic framework for direct learning of segregation energies.
  • Utilizing ab initio calculations to bypass the need for interatomic potentials.
  • Creation of an extensive GB segregation database for aluminum-based alloys.

Main Results:

  • The framework accurately predicts the full spectrum of solute segregation energies.
  • An extensive database of GB solute segregation for aluminum-based alloys was generated.
  • The study provides data for dozens of alloys with limited prior segregation information.

Conclusions:

  • The developed framework enables a comprehensive, quantum-accurate catalog of GB solute segregation for the entire alloy space.
  • This approach significantly advances the understanding of solute-GB interactions in metallic materials.
  • The initial database for aluminum alloys serves as a foundation for future materials design.