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The $GW$ method is widely used for calculating the electronic band structure of materials. The high computational cost of $GW$ algorithms prohibits their application to many systems of interest. We present a periodic, low-scaling and highly…

We analyze a data set comprising 370 GW band structures composed of 61716 quasiparticle (QP) energies of two-dimensional (2D) materials spanning 14 crystal structures and 52 elements. The data results from PAW plane wave based one-shot…

Materials Science · Physics 2021-01-05 Asbjørn Rasmussen , Thorsten Deilmann , Kristian S. Thygesen

We present quasiparticle (QP) energies from fully self-consistent $GW$ (sc$GW$) calculations for a set of prototypical semiconductors and insulators within the framework of the projector-augmented wave methodology. To obtain converged…

Materials Science · Physics 2018-10-31 Manuel Grumet , Peitao Liu , Merzuk Kaltak , Jiří Klimeš , Georg Kresse

$\mathtt{StochasticGW}$ is a code for computing accurate Quasi-Particle (QP) energies of molecules and material systems in the GW approximation. $\mathtt{StochasticGW}$ utilizes the stochastic Resolution of the Identity (sROI) technique to…

Despite its success in the study of spectroscopic properties, the $GW$ method presents specific methodological challenges when applied to systems with metallic screening. Here, we present an efficient and fully ab-initio implementation for…

The GW approximation has been widely accepted as an ab initio tool for calculating defect levels with many-electron effect included. However, the GW simulation cost increases dramatically with the system size, and, unfortunately, large…

Materials Science · Physics 2023-10-04 Du Li , Zhen-Fei Liu , Li Yang

Accurately calculating band gaps for given crystal structures is highly desirable. However, conventional first-principles calculations based on density functional theory (DFT) within the local density approximation (LDA) fail to predict…

Materials Science · Physics 2025-07-28 Shota Tankano , Takao Kotani , Masao Obata , Kazunori Sato , Harutaka Saito , Tatsuki Oda

The GW approximation is a well-known method to improve electronic structure predictions calculated within density functional theory. In this work, we have implemented a computationally efficient GW approach that calculates central…

An efficient all-electron G$^0$W$^0$ method and a quasiparticle selfconsistent GW (QSGW) method for molecules are proposed in the molecular orbital space with the full random phase approximation. The convergence with basis set is examined.…

Mesoscale and Nanoscale Physics · Physics 2015-03-17 San-Huang Ke

The $GW$ approximation has been recently gaining popularity among the method for simulating molecular core-level X-ray photoemission spectra. Traditionally, $GW$ core-level binding energies have been computed using either the cc-pV$n$Z or…

Chemical Physics · Physics 2022-07-13 Daniel Mejia-Rodriguez , Alexander Kunitsa , Edoardo Aprà , Niranjan Govind

The search for new materials, based on computational screening, relies on methods that accurately predict, in an automatic manner, total energy, atomic-scale geometries, and other fundamental characteristics of materials. Many…

Materials Science · Physics 2017-10-25 M. J. van Setten , M. Giantomassi , X. Gonze , G. -M. Rignanese , G. Hautier

The $GW$ approximation to many-body perturbation theory is a reliable tool for describing charged electronic excitations, and it has been successfully applied to a wide range of extended systems for several decades using a plane-wave basis.…

Materials Science · Physics 2019-10-23 Young-Moo Byun , Serdar Öğüt

The good performance of the GW approximation for band-structure calculations in solids was long taken as a sign that the sum of self-energy diagrams is converged and that all omitted terms are small. However, with modern computational…

Materials Science · Physics 2007-05-23 Arno Schindlmayr

We present an accurate approach to compute X-ray photoelectron spectra based on the $GW$ Green's function method, that overcomes shortcomings of common density functional theory approaches. $GW$ has become a popular tool to compute valence…

Chemical Physics · Physics 2020-03-11 Dorothea Golze , Levi Keller , Patrick Rinke

We report an all-electron, atomic orbital (AO) based, two-component (2C) implementation of the $GW$ approximation (GWA) for closed-shell molecules. Our algorithm is based on the space-time formulation of the GWA and uses analytical…

Chemical Physics · Physics 2023-09-11 Arno Förster , Erik van Lenthe , Edoardo Spadetto , Lucas Visscher

$GW$ is an accurate method for computing electron addition and removal energies of molecules and solids. In a conventional $GW$ implementation, however, its computational cost is $O(N^4)$ in the system size $N$, which prohibits its…

Chemical Physics · Physics 2021-03-22 Jan Wilhelm , Patrick Seewald , Dorothea Golze

We have implemented the so called GW approximation (GWA) based on an all-electron full-potential Projector Augmented Wave (PAW) method. For the screening of the Coulomb interaction W we tested three different plasmon-pole dielectric…

Materials Science · Physics 2007-05-23 B. Arnaud , M. Alouani

Finding an accurate ab initio approach for calculating the electronic properties of transition metal oxides has been a problem for several decades. In this paper, we investigate the electronic structure of the transition metal monoxides…

Strongly Correlated Electrons · Physics 2015-06-23 Suvadip Das , John E. Coulter , Efstratios Manousakis

In past decades the scientific community has been looking for a reliable first-principles method to predict the electronic structure of solids with high accuracy. Here we present an approach which we call the quasiparticle self-consistent…

Materials Science · Physics 2009-11-11 M. van Schilfgaarde , T. Kotani , S. Faleev

In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks. Armed with the Luo-Tseng error bound…

Machine Learning · Computer Science 2022-12-15 Jiajin Li , Jianheng Tang , Lemin Kong , Huikang Liu , Jia Li , Anthony Man-Cho So , Jose Blanchet