Uncovering Clar's aromatic -sextet rule in the Hubbard model using Maximum Probability Domain Partitions

Area of Science:

• Quantum chemistry
• Theoretical chemistry
• Chemical physics

Background:

• Clar's aromatic sextet rule is a qualitative tool for analyzing polycyclic benzenoid hydrocarbons.
• Existing quantum chemical explanations for Clar's rule have limitations in their applicability.

Purpose of the Study:

• To investigate the theoretical underpinnings of Clar's aromatic sextet rule.
• To demonstrate how electron distribution probabilities support the rule in various quantum chemical regimes.

Main Methods:

• Analysis of electron distribution over domain partitions.
• Exploration within mean-field and static correlation frameworks.
• Identification of domain partitions that maximize fundamental probabilities.

Main Results:

• Fundamental probabilities associated with electron distribution support Clar's rule.
• Domain partitions maximizing these probabilities correlate with dominant Clar structures.
• The findings hold true in both mean-field and static correlation regimes.

Conclusions:

• The study provides a probabilistic foundation for Clar's aromatic sextet rule.
• Maximizing electron distribution probabilities offers a novel quantum chemical basis for the rule.
• Extending probability-maximization methods could yield new quantum chemical concepts.