Full-frequency GW from Cayley-transformed self-energy moments
physics.chem-ph, cond-mat.str-el, physics.comp-ph
Submitted: 2026-09-24
Updated: 2026-10-03
License: http://creativecommons.org/licenses/by/4.0/
The gist: The dynamical GW self-energy approximation is a key computational tool to provide the fundamental spectrum of electronic systems.
Terminology
Abstract
The dynamical GW self-energy approximation is a key computational tool to provide the fundamental spectrum of electronic systems. We reformulate this approximation, representing the particle and hole parts of the GW self-energy through a highly compact set of Cayley-transformed moment constraints. The Cayley transformation maps real frequencies to the unit circle, keeping the moments bounded as their order increases, ensuring numerical stability and allowing resolution to be focused on an energy range of interest. We calculate these Cayley-transformed moments via an efficient O[N 4] scaling contour integration, and from them, construct a Hermitian upfolded Hamiltonian with a linearly scaling dimensionality with system size. A single-shot diagonalization of this effective Hamiltonian gives an explicit full-frequency G0W0 Green's function with manifestly real poles and non-negative spectral weights. This enables quasiparticle energies, satellite features, and their spectral weights to be obtained across the full G0W0 spectrum. Comparisons with exact G0W0 calculations and convergence across the GW100 test set and the larger Chlorophyll A molecule demonstrate substantially faster and more reliable convergence with moment order than an earlier monomial-moment approach. These Cayley moment representations therefore provide a stable, compact, and systematically improvable route to the complete spectral information of zero-temperature GW, without explicit frequency grids, plasmon-pole models and other common approximations, or analytic continuation.
Sources
- Moment problems and bounds for matrix-valued smeared spectral functions
- Compact and Stable Representation of Real-Frequency Spectral Functions for Machine Learning
- The truncated matrix trigonometric moment problem with an open gap
- The Analytic Theory of Matrix Orthogonal Polynomials
- The truncated matrix trigonometric moment problem: the operator approach
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