$G^0W^0$ implementation based on the pseudopotential and numerical-atomic-orbital basis-set framework: Algorithms and benchmarks
摘要
The method delivers substantially improved accuracy in electronic band structure calculations over conventional Kohn-Sham density functional theory (KS-DFT) by explicitly incorporating the electron self-energy effect beyond mean-field approximations. Despite many existing implementations, a periodic implementation within the framework of numerical atomic orbitals (NAO) combined with the pseudopotential (PP) scheme has not been reported. This is urgently needed given the increasing popularity of the NAO-PP framework in KS-DFT calculations and its importance for the development of machine-learning electronic-structure approaches. In this work, we present an efficient NAO-PP-based computational framework by interfacing the first-principles software package ABACUS with LibRPA -- a library for performing low-scaling random-phase approximation and calculations based on NAOs. Our approach employs the localized resolution of identity (LRI) technique with a novel compression scheme, significantly improving both computational efficiency and numerical stability. In addition, an analytic treatment of the small-q limit of the microscopic dielectric function reduces the need for dense q-point sampling. Furthermore, we propose a practical strategy to select a suitable KS-DFT pseudopotential prior to calculations by examining the frequency-dependent macroscopic dielectric function. Systematic benchmarks validate the effectiveness of our compression scheme and real-space tensor filtering strategies, demonstrating both high accuracy and significant computational efficiency gains. Comparisons with established implementations show excellent agreement in band structures and band gaps, confirming ABACUS+LibRPA as a reliable and efficient platform for large-scale simulations.
引用
@article{arxiv.2605.11512,
title = {$G^0W^0$ implementation based on the pseudopotential and numerical-atomic-orbital basis-set framework: Algorithms and benchmarks},
author = {Huanjing Gong and Min-Ye Zhang and Peize Lin and Bohan Jia and Ziqing Guan and Lixin He and Xinguo Ren},
journal= {arXiv preprint arXiv:2605.11512},
year = {2026}
}
备注
50 pages, 11 figures