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This study introduces an improved Ginzburg-Landau (GL) model to accurately predict solid-liquid interfacial free energy (γ) in FCC systems. The refined model offers reliable and efficient predictions, surpassing previous methods.

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Area of Science:

  • Materials Science
  • Computational Physics
  • Chemical Engineering

Background:

  • Predicting solid-liquid interfacial free energy (γ) is crucial for understanding material properties.
  • Existing models like Ginzburg-Landau (GL) and phase-field crystal models have limitations in accuracy and efficiency.
  • Atomistic simulation data can refine theoretical models for improved predictions.

Purpose of the Study:

  • To develop and validate a theoretical framework for predicting the solid-liquid interfacial free energy (γ) of FCC systems.
  • To enhance the accuracy and reliability of interfacial free energy predictions using a refined two-mode GL model.
  • To provide insights into factors governing interfacial free energy and guide property tuning.

Main Methods:

  • Utilized the two-mode Ginzburg-Landau (GL) model, incorporating atomistic simulation data.
  • Focused on Lennard-Jones (LJ) systems at the p-T two-phase coexistence boundary.
  • Employed equilibrium molecular dynamics simulations and analytical minimization to obtain interfacial density wave amplitude profiles.

Main Results:

  • The refined two-mode GL model accurately predicted γ and its anisotropy for FCC solid-liquid interfaces (SLIs).
  • Predictions showed strong agreement with benchmark computational studies, exceeding the accuracy of prior GL and phase-field crystal models.
  • The model demonstrated computational efficiency and quantitative reliability.

Conclusions:

  • The refined two-mode GL model is a computationally efficient and quantitatively reliable tool for predicting solid-liquid interfacial free energy (γ).
  • The model offers valuable insights into interfacial properties and can guide material design.
  • Future work includes refining variational procedures and exploring GL model extensions for enhanced predictive accuracy.