Range-Separated Density-Functional Theory in Combination with the Random Phase Approximation: An Accuracy Benchmark.

Area of Science:

• Computational chemistry
• Quantum chemistry
• Theoretical chemistry

Background:

• Accurate prediction of molecular properties is crucial in chemistry.
• Semilocal density functional theory (DFT) has limitations in describing certain chemical phenomena.
• Random Phase Approximation (RPA) offers a more accurate, albeit computationally expensive, alternative.

Purpose of the Study:

• To present a range-separated formulation of RPA using the efficient ω-CDGD-RI-RPA method.
• To conduct a large-scale benchmark study of this method on the GMTKN55 dataset.
• To evaluate its performance across thermochemistry, kinetics, and noncovalent interactions for general main group chemistry.

Main Methods:

• Implementation of a range-separated random phase approximation (RPA) formulation.
• Utilizing the efficient ω-CDGD-RI-RPA computational approach.
• Benchmarking against the comprehensive GMTKN55 dataset.

Main Results:

• Range-separated RPA demonstrates stable performance across diverse molecular chemistry within the GMTKN55 set.
• The method shows significant improvement over semilocal DFT.
• It exhibits faster basis set convergence compared to standard full-range RPA.

Conclusions:

• Range-separated RPA is a promising computational chemistry approach for general main group thermochemistry, kinetics, and noncovalent interactions.
• While less accurate than dispersion-corrected double-hybrid functionals, it offers a good balance of accuracy and efficiency.
• Its faster basis set convergence makes it a practical alternative for large-scale studies.