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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Implementation of an Effective Bond Energy Formalism in the Multicomponent Calphad Approach.

Nathalie Dupin1, Ursula R Kattner2, Bo Sundman3

  • 1Calcul Thermodynamique, 63670 Orcet, France.

Journal of Research of the National Institute of Standards and Technology
|December 8, 2021
PubMed
Summary

This study introduces a new model for calculating phase diagrams (Calphad) that improves extrapolation behavior in complex systems. The method partitions Gibbs energy, reducing parameters and computational time for materials science applications.

Keywords:
Calphadcompound energy formalismeffective bond energy formalism

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Thermodynamics

Background:

  • Current compound energy formalism models for phase diagram calculations (Calphad) exhibit poor extrapolation for complex systems.
  • This limitation is particularly evident when dealing with complex crystal structures.

Purpose of the Study:

  • To propose a novel model for Calphad calculations that enhances extrapolation behavior.
  • To reduce the number of parameters and computational time for phase diagram calculations.

Main Methods:

  • A partition of Gibbs energy into effective bond energies is proposed.
  • The configurational entropy expression remains unchanged.
  • The model accommodates sublattices corresponding to occupied Wyckoff sites.

Main Results:

  • The proposed model significantly improves extrapolation behavior in higher-order systems.
  • Demonstrated effectiveness for face-centered cubic (fcc) ordering and the sigma (σ) phase.
  • Potential for reducing parameter count and accelerating computational time.

Conclusions:

  • The novel Gibbs energy partitioning offers a more robust approach for Calphad calculations.
  • This method is particularly beneficial for complex crystal structures and higher-order systems.
  • The model shows promise for efficient and accurate phase diagram predictions.