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Published on: August 1, 2016
Strongly contracted canonical transformation theory
Eric Neuscamman1, Takeshi Yanai, Garnet Kin-Lic Chan
1Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA. eric.neuscamman@gmail.com
Canonical Transformation (CT) theory addresses dynamic correlation in large systems. New excitation operators effectively handle intruder states, enabling accurate modeling of complex molecules like porphin.
Area of Science:
- Quantum Chemistry
- Computational Chemistry
- Theoretical Chemistry
Background:
- Canonical Transformation (CT) theory models dynamic correlation in multireference systems.
- Large active spaces present challenges, including the intruder state problem.
- Previous methods like overlap matrix truncation become computationally infeasible for large systems.
Purpose of the Study:
- To address the intruder state problem in CT theory for large active spaces.
- To introduce and evaluate strongly and weakly contracted excitation operators as alternatives.
- To enable accurate modeling of dynamic correlation in complex molecular systems.
Main Methods:
- Developed and applied strongly and weakly contracted excitation operators within CT theory.
- Evaluated performance against complete active space second order perturbation theory (CASPT2) and Davidson-corrected multireference configuration interaction (MRCI+Q).
- Combined strongly contracted CT theory with orbital-optimized density matrix renormalization group (ODMRG) theory.
Main Results:
- The proposed excitation operators effectively manage intruder states in CT theory.
- Accurate calculations were performed for H2O, N2, and NiO molecules.
- The singlet-triplet gap of free base porphin was evaluated using a large active space (24 orbitals).
Conclusions:
- Strongly and weakly contracted excitation operators offer a viable solution to the intruder state problem in CT theory.
- CT theory, particularly when combined with ODMRG, can model dynamic correlation in very large active spaces.
- This approach is crucial for studying complex systems where traditional methods fail.
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