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We developed new global optimization methods for atomic clusters and nanoalloys. These approaches predict stable structures and compositions by analyzing potential energy minima, aiding materials science discovery.

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Area of Science:

  • Computational Materials Science
  • Statistical Mechanics
  • Chemical Physics

Background:

  • Determining the global minimum energy structure of atomic clusters and nanoalloys is computationally challenging.
  • Understanding phase transitions and stability as a function of temperature and chemical potential is crucial for materials design.

Purpose of the Study:

  • To introduce novel grand and semigrand canonical global optimization methods.
  • To apply these methods to study the temperature and chemical potential dependence of atomic cluster and nanoalloy stability.

Main Methods:

  • Basin-hopping global optimization incorporating an acceptance criterion based on local contributions to the (semi)grand potential.
  • Utilized local harmonic vibrational densities of states for analyzing atomic clusters.
  • Investigated multicomponent nanoalloys as a function of temperature and chemical potential.

Main Results:

  • Predicted global minima for atomic clusters transition from dissociated states to stable clusters at higher chemical potentials and lower temperatures.
  • Results align with predictions from a model fitted to experimental heat capacity data.
  • Semigrand canonical optimization successfully identified stable compositions in multicomponent nanoalloys across varying temperatures.

Conclusions:

  • The developed (semi)grand canonical global optimization methods are effective for predicting stable structures and compositions.
  • These methods provide insights into the thermodynamic stability of atomic clusters and nanoalloys.
  • Grand canonical potential analysis offers a byproduct survey of favorable structures during global optimization.