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Grand and Semigrand Canonical Basin-Hopping
F Calvo1,2, D Schebarchov3, D J Wales3
1Université Grenoble Alpes , LIPHY, F-38000 Grenoble, France.
Journal of Chemical Theory and Computation
|December 17, 2015
Summary
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.
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.
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