Noneigenstate Ab Initio Density Matrix Downfolding for Constructing Model Hamiltonians in Quantum Chemistry.

Area of Science:

• Quantum mechanics
• Computational chemistry

Background:

• Model Hamiltonians simplify complex quantum systems by capturing dominant interactions.
• Density matrix downfolding (DMD) systematically derives model Hamiltonians by matching energy spectra.
• DMD's applicability and performance in chemistry remain underexplored.

Purpose of the Study:

• To evaluate the performance of DMD for realistic chemical systems.
• To assess DMD's reliability and efficiency in deriving optimized model Hamiltonians.
• To explore DMD's potential for studying complex systems and understanding core physical principles.

Main Methods:

• Applied DMD using noneigenstates of ab initio Hamiltonians.
• Tested on diverse chemical systems: benzene, naphthalene, FeSe, and an Fe(IV)═O complex.
• Focused on systematically improvable derivation of model Hamiltonians.

Main Results:

• DMD proved to be a reliable tool for obtaining optimized model Hamiltonians.
• The method demonstrated computational efficiency for quantum chemistry applications.
• Successful application across various realistic chemical and biochemical systems.

Conclusions:

• DMD is a robust and efficient approach for developing accurate model Hamiltonians in chemistry.
• This facilitates the study of complex systems at lower computational cost.
• Enables deeper understanding of dominant physical principles for chemical design and tuning.