Random Phase Approximation Applied to Many-Body Noncovalent Systems

TL;DR

This paper evaluates the accuracy of the random phase approximation (RPA) in modeling noncovalent many-body interactions, compares its performance based on different DFT inputs, and introduces a cubic-scaling implementation for high-precision calculations.

Contribution

It provides new insights into RPA's accuracy for nonadditive interactions and presents a scalable, high-precision RPA implementation suitable for large systems.

Findings

RPA with SCAN0 and PBE0 models achieves accuracy between CCSD and MP3.

RPA effectively describes nonadditive three-body energies in clusters.

The new cubic-scaling RPA implementation enables high-precision calculations for large systems.

Abstract

The random phase approximation (RPA) has received a considerable interest in the field of modeling systems where noncovalent interactions are important. Its advantages over widely used density functional theory (DFT) approximations are the exact treatment of exchange and the description of long-range correlation. In this work we address two open questions related to RPA. First, how accurately RPA describes nonadditive interactions encountered in many-body expansion of a binding energy. We consider three-body nonadditive energies in molecular and atomic clusters. Second, how does the accuracy of RPA depend on input provided by different DFT models, without resorting to selfconsistent RPA procedure which is currently impractical for calculations employing periodic boundary conditions. We find that RPA based on the SCAN0 and PBE0 models, i.e., hybrid DFT, achieves an overall accuracy between CCSD and MP3 on a dataset of molecular trimers of \v{R}ez\'{a}\v{c} et al. (J. Chem. Theory. Comput. 2015, 11, 3065) Finally, many-body expansion for molecular clusters and solids often leads to a large number of small contributions that need to be calculated with a high precision. We therefore present a cubic-scaling (or SCF-like) implementation of RPA in atomic basis set, which is designed for calculations with a high numerical precision.