Realizing Effective Cubic-Scaling Coulomb Hole Plus Screened Exchange Approximation in Periodic Systems via Interpolative Separable Density Fitting with a Plane-Wave Basis Set

The GW approximation is an effective way to accurately describe the single-electron excitations of molecules and the quasiparticle energies of solids. However, a perceived drawback of the GW calculations is their high computational cost and large memory usage, which limit their applications to large systems. Herein, we demonstrate an accurate and effective low-rank approximation to accelerate non-self-consistent GW (G(0)W(0)) calculations under the static Coulomb hole plus screened exchange (COHSEX) approximation for periodic systems. Our approach is to adopt the interpolative separable density fitting (ISDF) decomposition and Cauchy's integral to construct low-rank representations of the dielectric matrix. and self-energy matrix Sigma. This approach reduces the number of floating point operations from O(N-e(4)) to O(N-e(3)) and requires a much smaller memory footprint. Two methods are used to select the interpolation points in ISDF, including the standard QR factorization with column pivoting (QRCP) procedure and the machine learning K-means clustering (K-means) algorithm. We demonstrate that these two methods can yield similar accuracy for both molecules and solids at much lower computational cost. In particular, K-means clustering can significantly reduce the computational cost of selecting the interpolation points by an order of magnitude compared to QRCP, resulting in an overall speedup factor of about ten times ISDF accelerated the static COHSEX calculations compared with conventional COHSEX approximation.

Automated summary

The study presents a low-rank approximation method to accelerate non-self-consistent GW calculations, utilizing ISDF decomposition and Cauchy's integral to construct low-rank representations and reduce computational cost and memory usage. Two methods for selecting interpolation points, including QRCP procedure and K-means clustering algorithm, achieve similar accuracy at lower computational cost, with K-means clustering significantly reducing the computational cost by an order of magnitude compared to QRCP.