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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
Rethinking linearized coupled-cluster theory
Andrew G Taube1, Rodney J Bartlett
1Sandia National Laboratories, Albuquerque, New Mexico 87185, USA. agtaube@sandia.gov
The Journal of Chemical Physics
|April 17, 2009
Summary
A new Tikhonov regularization method eliminates singularities in Hermitian linearized coupled-cluster (CC) calculations. This approach offers a robust and efficient alternative to conventional coupled-cluster methods for electronic structure research.
Area of Science:
- Quantum Chemistry
- Computational Chemistry
- Theoretical Chemistry
Background:
- Hermitian linearized coupled-cluster (CC) methods offer advantages like analytical gradients for potential energy surface searches.
- A significant drawback of linearized CC methods is the occurrence of singularities on the potential energy surface.
Purpose of the Study:
- To introduce a Tikhonov regularization procedure to eliminate singularities in Hermitian linearized coupled-cluster methods.
- To assess the performance of the regularized method for determining molecular structures and transition states.
Main Methods:
- A simple Tikhonov regularization procedure was implemented.
- The regularized linearized coupled-cluster singles and doubles (CCSD) method was applied to calculate equilibrium structures and transition states.
Main Results:
- The Tikhonov regularization effectively eliminated singularities from the potential energy surface.
- The regularized linearized CCSD method demonstrated performance competitive with or superior to conventional CCSD.
- The regularized method showed improved amenability to parallelization.
Conclusions:
- The proposed Tikhonov regularization is a viable solution for addressing singularities in linearized CC methods.
- This approach enhances the reliability and efficiency of electronic structure calculations.
- The regularized method is a promising tool for computational chemistry, particularly for large-scale parallel computations.
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