A genetic algorithm survey on closed-shell atomic nitrogen clusters employing a quantum chemical approach

Area of Science:

• Computational Chemistry
• Materials Science
• Quantum Chemistry

Background:

• Nitrogen clusters are of interest for their unique bonding and potential applications.
• Understanding their potential energy surfaces is crucial for predicting stability and reactivity.

Purpose of the Study:

• To explore the potential energy hypersurfaces of closed-shell nitrogen clusters up to ten atoms.
• To identify stable structures and compare their binding energies using various computational methods.

Main Methods:

• Utilized a genetic algorithm (GA) with modified evolutionary procedures.
• Employed density functional theory (DFT) with B3LYP and PBE functionals and a 6-31G basis set.
• Performed reoptimization and vibrational analysis using MP2 and CCSD(T) levels with larger basis sets.

Main Results:

• Confirmed previously reported polynitrogen structures.
• Predicted the stability of novel nitrogen cluster configurations.
• Calculated and compared binding energies and relative dissociation energies.

Conclusions:

• The genetic algorithm approach effectively identified stable nitrogen cluster structures.
• The choice of computational method significantly impacts the calculated energy surface profile.
• New stable polynitrogen structures were predicted, expanding the understanding of nitrogen cluster chemistry.