H·(H2O)n clusters: microsolvation of the hydrogen atom via molecular ab initio gradient embedded genetic algorithm...

Area of Science:

• Computational Chemistry
• Chemical Physics
• Quantum Chemistry

Background:

• Solvated hydrogen atoms (H·) are transient species in water, crucial for photochemistry and biological processes.
• Understanding their structure and energetics is key to resolving long-standing scientific debates.
• H·(H(2)O)(n) clusters (n=1-4) serve as fundamental models for solvated hydrogen atoms.

Purpose of the Study:

• To report a new version of the ab initio gradient embedded genetic algorithm (GEGA) for global minima searches.
• To demonstrate GEGA's performance on challenging H·(H(2)O)(n) clusters.
• To elucidate the structural diversity and energetics of these clusters.

Main Methods:

• Utilized the ab initio gradient embedded genetic algorithm (GEGA) for potential energy surface (PES) exploration.
• Applied GEGA to neutral H·(H(2)O)(n) clusters (n=1-4).
• Confirmed findings using higher levels of theory.

Main Results:

• Identified four basic structural types (I-IV) for H·(H(2)O)(n) clusters, with numerous isomers.
• Found that types I and II represent isoenergetic global minima.
• Demonstrated GEGA's success in locating global and low-energy local minima despite challenges like shallow minima and degeneracy.

Conclusions:

• GEGA effectively navigates complex potential energy landscapes, accurately characterizing H·(H(2)O)(n) clusters.
• The study explicates the structural diversity and energetic landscape of these fundamental clusters.
• GEGA proves to be a powerful tool for studying weakly bound systems prone to dissociation.