Multireference Equation-of-Motion Driven Similarity Renormalization Group: Theoretical Foundations and Applications
Zijun Zhao1, Shuhang Li1, Francesco A Evangelista1
1Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States.
We developed a new method, ionization potential equation-of-motion extension of the multireference driven similarity renormalization group (IP-EOM-DSRG), for accurately calculating ionization potentials in complex molecules. This approach offers a significant advancement for computational chemistry and spectroscopy.
Area of Science:
- Quantum chemistry
- Computational physics
- Spectroscopy
Background:
- Strongly correlated systems pose challenges for traditional electronic structure methods.
- Accurate calculation of ionization potentials is crucial for understanding molecular properties and reactivity.
Purpose of the Study:
- To introduce and implement the equation-of-motion extension of the multireference driven similarity renormalization group (IP-EOM-DSRG) formalism.
- To develop a robust and efficient method for computing ionization potentials of challenging molecular systems.
Main Methods:
- The IP-EOM-DSRG formalism is formulated as a Hermitian generalized eigenvalue problem.
- It is combined with three truncation schemes: MR-LDSRG(2), DSRG-MRPT2, and DSRG-MRPT3.
- The computational cost scales as O(N^5) with basis set size N.
Main Results:
- Accurate vertical valence ionization potentials were computed for small molecules.
- Spectroscopic constants for low-lying electronic states of radicals (OH, CN, N2+, CO+) were determined.
- Binding curves for electronic states of the CN radical were calculated.
Conclusions:
- All tested IP-EOM-DSRG variants accurately reproduce experimental ionization potentials and spectroscopic constants.
- The DSRG-MRPT3 and MR-LDSRG(2) methods show superior performance compared to other state-of-the-art techniques.
- The IP-EOM-DSRG formalism provides an efficient and accurate tool for studying strongly correlated systems.
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