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August 16, 2025Physical Review Materials

Thermodynamical phase-stability of a Cu-Al binary system using machine-learning interatomic potentials

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Authors

EAE. AntillonNBNoam Bernstein

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Overview

This analysis demonstrates phase stability in the Cu-Al system, suggesting that machine-learning models effectively predict thermodynamic properties.

Key Points

  • Machine-learning interatomic potentials can accurately determine free energy differences in the Cu-Al binary alloy.
  • Stable phases include liquid, face-centered cubic, and body-centered cubic, validating against experimental data.
  • Thermodynamic integration methods were applied to explore the phase behavior across temperature and composition.
  • The research highlights the potential of machine-learning models to enhance phase stability calculations in materials science.

Cite This Study

Antillon et al. (2025) studied this question.

synapsesocial.com/papers/68a368920a429f797332df10https://doi.org/10.1103/vtyl-j1nz
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