Highly Parallel Real-Space Random Phase Approximation Using Lanczos Quadrature and Interpolation
Abstract
We present a highly parallelizable, matrix-free, real-space method for computing the random phase approximation (RPA) correlation energy within Kohn-Sham density functional theory. In particular, we avoid the explicit construction and eigen-decomposition of the response function matrix and use Lanczos quadrature to evaluate the trace of a matrix function on a real-space grid. We also show it is possible to exploit the spatial smoothness of the RPA correlation energy density in real-space to reduce computational cost. Specifically, we compute the energy density on a coarse grid, then reconstruct an approximation to the full, fine grid via interpolation. We implement this formulation within the SPARC electronic structure package and demonstrate its convergence, accuracy, agreement with planewave results, and scaling. Interpolation can enable an 8× reduction in the computational prefactor. Given the embarrassingly parallel nature of the proposed method, we achieve near-ideal speedups and near-cubic scaling, thus allowing us to compute the RPA correlation energy for a silicon system with 256 valence electrons at chemical accuracy in less than 30 minutes on 4,096 CPU cores.
Related
- Utilizing a Perturbative Inverse of the Hamiltonian to Improve Lanczos Diagonalization
- Quadrature-detected zero-to-ultralow-field nuclear magnetic resonance
- Translation-rotation internal coordinates with splicing (TRICS): A new approach to molecular geometry interpolation
- Anti-Parallel Dimer Structure of p-Cyanophenol Adsorbed at the Aqueous Solution Surface Probed by Sub-1 cm-1 High Resolution
- Parallel mechanochemical synthesis of phenolic drug–poly(lactic acid) conjugates using a multi-tube BenchMixer/vortexer
BibTeX
@misc{haque2026highly,
title = {{Highly Parallel Real-Space Random Phase Approximation Using Lanczos Quadrature and Interpolation}},
author = {Abir Haque and Edmond Chow and Shikhar Shah and Andrew J. Medford and John E. Pask and Phanish Suryanarayana},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.16408},
eprint = {2609.16408},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.16408},
}
RIS
TY - UNPB
TI - Highly Parallel Real-Space Random Phase Approximation Using Lanczos Quadrature and Interpolation
AU - Abir Haque
AU - Edmond Chow
AU - Shikhar Shah
AU - Andrew J. Medford
AU - John E. Pask
AU - Phanish Suryanarayana
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.16408
UR - https://arxiv.org/abs/2609.16408
AB - We present a highly parallelizable, matrix-free, real-space method for computing the random phase approximation (RPA) correlation energy within Kohn-Sham density functional theory. In particular, we avoid the explicit construction and eigen-decomposition of the response function matrix and use Lanczos quadrature to evaluate the trace of a matrix function on a real-space grid. We also show it is possible to exploit the spatial smoothness of the RPA correlation energy density in real-space to reduce computational cost. Specifically, we compute the energy density on a coarse grid, then reconstruct an approximation to the full, fine grid via interpolation. We implement this formulation within the SPARC electronic structure package and demonstrate its convergence, accuracy, agreement with planewave results, and scaling. Interpolation can enable an 8× reduction in the computational prefactor. Given the embarrassingly parallel nature of the proposed method, we achieve near-ideal speedups and near-cubic scaling, thus allowing us to compute the RPA correlation energy for a silicon system with 256 valence electrons at chemical accuracy in less than 30 minutes on 4,096 CPU cores.
ER -