Structure and spectroscopy of water-fluoride microclusters: a combined genetic algorithm and DFT-based study.

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Materials Science

Background:

• Investigating the structure and properties of micro-clusters is crucial for understanding chemical interactions.
• Water-fluoride clusters are relevant in various chemical and environmental processes.
• Accurate structural determination is essential for predicting spectroscopic properties.

Purpose of the Study:

• To evaluate the efficiency of a combined genetic algorithm (GA) and density functional theory (DFT) approach.
• To determine probable structures of (H2O)nF- micro-clusters for n = 1-6.
• To calculate and analyze the infra-red spectra of these micro-clusters.

Main Methods:

• Utilizing a genetic algorithm (GA) for stochastic optimization to find stable cluster structures.
• Employing density functional theory (DFT) calculations with optimized coordinates from GA.
• Comparing the obtained infra-red spectra with established quantum chemical results.

Main Results:

• The coupled GA-DFT strategy successfully identified probable structures for (H2O)nF- micro-clusters.
• Calculated infra-red spectra closely matched reference quantum chemical data.
• The method demonstrated high efficiency in predicting cluster properties.

Conclusions:

• The GA-DFT approach is an efficient and reliable tool for studying micro-cluster systems.
• This strategy provides accurate structural and spectroscopic information for water-fluoride clusters.
• The findings validate the computational approach for similar chemical investigations.