Generalized Self-Energy Embedding Theory.

Area of Science:

• Quantum Chemistry
• Computational Chemistry
• Materials Science

Background:

• Ab initio quantum chemistry calculations are essential for understanding molecular behavior.
• Standard methods face significant computational challenges with large active spaces.
• Accurate treatment of large active spaces is crucial for complex chemical systems.

Purpose of the Study:

• To generalize self-energy embedding theory (SEET) for tackling large active spaces in quantum chemistry.
• To develop a method capable of achieving results comparable to full active-space treatments.
• To introduce a hierarchy of systematically improvable approximations within the generalized SEET framework.

Main Methods:

• Generalization of the Green's function QM/QM embedding method, SEET.
• Grouping of active orbitals into intersecting subsets for parallel computation.
• Application and examination of generalized SEET on various chemical systems.

Main Results:

• Generalized SEET successfully treats systems with large active spaces, overcoming limitations of standard methods.
• Parallel calculations on smaller orbital groups yield results comparable to computationally expensive full active-space treatments.
• Demonstrated a hierarchy of approximations for systematic improvement of accuracy.

Conclusions:

• Generalized SEET provides a computationally feasible and accurate approach for large active space problems.
• The method offers a scalable and versatile tool for advanced quantum chemistry calculations.
• This work paves the way for studying more complex molecular systems with high fidelity.