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Spin-unrestricted random-phase approximation with range separation: Benchmark on atomization energies and reaction
Bastien Mussard1, Peter Reinhardt2, János G Ángyán3
1Sorbonne Universités, UPMC Univ Paris 06, Institut du Calcul et de la Simulation, F-75005 Paris, France.
The Journal of Chemical Physics
|April 24, 2015
Summary
We evaluated random-phase approximation (RPA) methods for calculating energies. Range-separated RPAx-SO2 proved most accurate for atomization energies and reaction barriers, showing promise for general chemical applications.
Area of Science:
- Quantum Chemistry
- Computational Chemistry
- Theoretical Chemistry
Background:
- Accurate calculation of correlation energies is crucial for predicting chemical properties.
- Random-Phase Approximation (RPA) methods offer a computationally tractable approach to correlation energy.
- Range separation is a technique to improve the accuracy of electronic structure calculations.
Purpose of the Study:
- To evaluate various spin-unrestricted RPA variants for calculating correlation energies.
- To assess the impact of range separation on RPA accuracy for atomization energies and reaction barriers.
- To identify the most accurate RPA variant for general chemical applications.
Main Methods:
- Testing several spin-unrestricted RPA variants, with and without range separation.
- Utilizing datasets of atomization energies and reaction barrier heights for evaluation.
- Extending the RPAx-SO2 method to a spin-unrestricted formalism.
Main Results:
- Range separation significantly enhances the accuracy of all tested RPA variants.
- The range-separated RPAx-SO2 method demonstrates superior average accuracy for atomization energies and reaction barrier heights.
- RPAx-SO2 was previously shown to be accurate for intermolecular interactions.
Conclusions:
- Range-separated RPAx-SO2 is a highly accurate and promising method for general chemical applications.
- The findings confirm the effectiveness of range separation in improving RPA-based calculations.
- RPAx-SO2 offers a robust approach for predicting key chemical properties.
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