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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Nanotechnology

Background:

  • Determining the lowest-energy structures of bimetallic clusters is computationally challenging due to numerous isomers.
  • Existing methods for global optimization of alloy clusters can be time-consuming and may not always find the global minimum.

Purpose of the Study:

  • To develop and validate a novel basin hopping genetic algorithm (BHGA) for efficient global structure searching of bimetallic clusters.
  • To investigate the structural properties and relative stabilities of platinum-cobalt (PtCo) clusters with 13, 38, and 55 atoms.

Main Methods:

  • Implementation of a basin hopping genetic algorithm (BHGA) combining genetic algorithms with basin hopping.
  • Global optimization of 13-, 38-, and 55-atom PtCo bimetallic clusters.
  • Analysis of cluster stability using excess energy and the second difference of energy.
  • Structural characterization using bond order parameter, similarity function, and shape factor.

Main Results:

  • The BHGA significantly improves convergent speed and the probability of finding global minima compared to the standard basin hopping method.
  • Initializing with monometallic structures enhances searching efficiency.
  • 13- and 55-atom PtCo clusters predominantly adopt icosahedral structures with varying lattice distortions.
  • 38-atom PtCo clusters exhibit diverse structures, including disordered, defected face-centered cubic (fcc), and perfect fcc arrangements.

Conclusions:

  • The proposed BHGA is an effective tool for the global optimization of bimetallic clusters.
  • The study provides insights into the structural preferences and stability of PtCo alloy clusters.
  • The methodology offers a valuable reference for exploring other alloy cluster systems.