Tensor hypercontracted ppRPA: reducing the cost of the particle-particle random phase approximation from O(r(6)) to O(r(4)).

Journal Article (Journal Article)

In recent years, interest in the random-phase approximation (RPA) has grown rapidly. At the same time, tensor hypercontraction has emerged as an intriguing method to reduce the computational cost of electronic structure algorithms. In this paper, we combine the particle-particle random phase approximation with tensor hypercontraction to produce the tensor-hypercontracted particle-particle RPA (THC-ppRPA) algorithm. Unlike previous implementations of ppRPA which scale as O(r(6)), the THC-ppRPA algorithm scales asymptotically as only O(r(4)), albeit with a much larger prefactor than the traditional algorithm. We apply THC-ppRPA to several model systems and show that it yields the same results as traditional ppRPA to within mH accuracy. Our method opens the door to the development of post-Kohn Sham functionals based on ppRPA without the excessive asymptotic cost of traditional ppRPA implementations.

Full Text

Duke Authors

Cited Authors

  • Shenvi, N; van Aggelen, H; Yang, Y; Yang, W

Published Date

  • July 2014

Published In

Volume / Issue

  • 141 / 2

Start / End Page

  • 024119 -

PubMed ID

  • 25028011

Electronic International Standard Serial Number (EISSN)

  • 1089-7690

International Standard Serial Number (ISSN)

  • 0021-9606

Digital Object Identifier (DOI)

  • 10.1063/1.4886584


  • eng