Complexity Reduction in Large Quantum Systems: Fragment Identification and Population Analysis via a Local Optimized...

Area of Science:

• Computational chemistry
• Quantum mechanics
• Materials science

Background:

• Fragmenting large quantum systems is challenging.
• Accurate electrostatic multipole extraction is crucial for understanding molecular properties.
• Existing methods often lack quantitative rigor and introduce arbitrariness.

Purpose of the Study:

• To present a quantitative method for system fragmentation within Kohn-Sham density functional theory.
• To enable reliable extraction of electrostatic multipoles for identified fragments.
• To reduce arbitrariness in fragmentation and assess the observability of fragment properties.

Main Methods:

• Kohn-Sham density functional theory (KS-DFT) calculations.
• Generalizations of population analyses for electrostatic multipole extraction.
• Application within the BigDFT code.
• Use of in situ-optimized basis functions.

Main Results:

• A quantitative method to identify and partition large quantum systems into fragments.
• Reliable extraction of electrostatic multipoles from first-principles.
• Reduced arbitrariness in the fragmentation procedure.
• Demonstration that fragment multipoles can be interpreted as observable quantities.
• Accurate description of electronic structure with minimal basis functions.

Conclusions:

• The developed method provides a robust framework for fragmenting quantum systems.
• It allows for quantitative assessment of fragment properties, specifically electrostatic multipoles.
• The approach enhances the interpretability of fragment properties as observable quantities.
• Efficient and accurate electronic structure descriptions are achievable using in situ-optimized basis sets.