Reference Determinant Dependence of the Random Phase Approximation in 3d Transition Metal Chemistry.
J E Bates1, P D Mezei2, G I Csonka2
1Department of Physics, Temple University , Philadelphia, Pennsylvania 19122, United States.
Journal of Chemical Theory and Computation
|December 21, 2016
Summary
The Random Phase Approximation (RPA) shows promise for predicting transition metal chemistry, reducing errors from semilocal functionals. Its accuracy depends on the exact exchange mixing in reference determinants, with high mixing potentially degrading performance.
Area of Science:
- Computational Chemistry
- Quantum Chemistry
- Materials Science
Background:
- Accurate prediction of transition metal chemistry is difficult with standard density functional approximations.
- The Random Phase Approximation (RPA) shows potential beyond main group thermochemistry.
- Limited understanding exists regarding RPA's performance for transition metal systems.
Purpose of the Study:
- To analyze the performance of RPA for transition metal reaction energies, barrier heights, and ligand dissociation energies.
- To compare RPA results with semilocal and hybrid density functionals.
- To investigate the reference determinant dependence of RPA, particularly concerning exact exchange (EXX) mixing.
Main Methods:
- Calculations of reaction energies, barrier heights, and ligand dissociation energies using RPA.
- Comparison with various semilocal and hybrid density functionals.
- Analysis of RPA performance with different reference determinants, varying exact exchange fractions.
Main Results:
- RPA systematically reduces errors compared to semilocal functionals for transition metal chemistry.
- RPA provides excellent performance from a single reference determinant, even for multireference reactions.
- High fractions of exact exchange (EXX) in reference determinants can degrade RPA performance.
Conclusions:
- RPA offers a robust method for improving predictions in transition metal chemistry.
- The choice of reference determinant and its exact exchange content is crucial for RPA accuracy.
- Dual hybrid functionals combining RPA correlation did not show systematic improvement over traditional RPA for these systems.
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