Size-guided multi-seed heuristic method for geometry optimization of clusters: Application to benzene clusters.

Area of Science:

• Computational chemistry
• Materials science
• Chemical physics

Background:

• Global minimum searches on potential energy surfaces are computationally challenging.
• Existing geometry optimization methods often rely on random initial configurations.
• Developing efficient methods for finding stable molecular cluster structures is crucial.

Purpose of the Study:

• To develop a novel size-guided multi-seed heuristic method for locating global minima of molecular clusters.
• To apply and evaluate this method for benzene clusters of varying sizes.

Main Methods:

• A size-guided multi-seed heuristic approach was developed.
• Initial geometries for clusters of size n were derived from optimal configurations of clusters with n-1 molecules.
• Geometrical perturbations were used for further optimization.
• The method was applied to benzene clusters up to 65 molecules.

Main Results:

• The developed method successfully located putative global minima for benzene clusters up to 65 molecules.
• Performance was assessed based on computational cost, global minimum location rates, and initial geometry energies.
• The method demonstrates efficiency in exploring the potential energy surface.

Conclusions:

• The size-guided multi-seed heuristic method is effective for finding global minima of benzene clusters.
• This approach offers an improvement over purely random initial geometry generation.
• The study provides insights into the computational performance and success rates of the new method.