Merge branch 'main' into joss_responses

# Conflicts:
#	JOSS/refs.bib

JOSS/paper.md

@@ -67,7 +67,7 @@ The package comprises minimax time and frequency grids [@Takatsuka2008; @kaltak2
 
 RPA is an accurate approach to compute the electronic correlation energy. It is non-local, includes long-range dispersion interactions and dynamic electronic screening, and is applicable to a wide range of systems from 0 to 3 dimensions [@eshuis2012electron; @ren2012random]. The \textit{GW} method [@hedin1965new] is based on the RPA susceptibility and has become the method of choice for the calculation of direct and inverse photoemission spectra of molecules and solids [@golze2019gw;@reining2018gw;@stankovski2011g;@li2005quasiparticle;@bruneval2008accurate;@nabok2016accurate]. \textit{GW} forms the basis for Bethe-Salpeter Equation (BSE) calculations of optical spectra [@onida2002electronic], where the \textit{GW} results are used as input.
 
-Despite their wide adoption, RPA and \textit{GW} face computational challenges, especially for large systems. Conventional RPA and \textit{GW} implementations scale with the fourth power of the system size $N$ and are therefore usually limited to systems of a few ten to at most hundred atoms [@delben2013electron; @wilhelm2016gw; @stuke2020atomic]. To tackle larger and more realistic systems, scaling reductions present a promising strategy to decrease the computational cost. Such low-scaling algorithms utilize real-space representations and time-frequency transformations, such as the real-space/imaginary-time approach [@rojas1995space] that reduces the complexity to $\mathcal{O}(N^3)$. Several such cubic-scaling \textit{GW} algorithms have recently been implemented, e.g. in a plane-wave/projector-augmented-wave (PAW) \textit{GW} code [@kaltak2014low;@liu2016cubic] or with localized basis sets using Gaussian [@wilhelm2018toward;@wilhelm2021low;@duchemin2021cubic] or Slater-type orbitals [@forster2020low;@foerster2021GW100;@foerster2021loworder;@foerster2023twocomponent]. Similarly, low-scaling RPA algorithms were implemented with different basis sets [@kaltak2014cubic;@kaltak2014low;@wilhelm2016rpa;@luenser2017;@graf2018accurate;@duchemin2019separable;@drontschenko2022efficient;@kutepov2012electronic;@kutepov2017linearized].
+Despite their wide adoption, RPA and \textit{GW} face computational challenges, especially for large systems. Conventional RPA and \textit{GW} implementations scale with the fourth power of the system size $N$ and are therefore usually limited to systems of a few ten to at most hundred atoms [@delben2013electron; @wilhelm2016gw; @stuke2020atomic]. To tackle larger and more realistic systems, scaling reductions present a promising strategy to decrease the computational cost. Such low-scaling algorithms utilize real-space representations and time-frequency transformations, such as the real-space/imaginary-time approach [@rojas1995space] that reduces the complexity to $\mathcal{O}(N^3)$. Several such cubic-scaling \textit{GW} algorithms have recently been implemented, e.g. in a plane-wave/projector-augmented-wave (PAW) \textit{GW} code [@kaltak2014low;@liu2016cubic] or with localized basis sets using Gaussian [@wilhelm2018toward;@wilhelm2021low;@duchemin2021cubic;@Graml2023] or Slater-type orbitals [@forster2020low;@foerster2021GW100;@foerster2021loworder;@foerster2023twocomponent]. Similarly, low-scaling RPA algorithms were implemented with different basis sets [@kaltak2014cubic;@kaltak2014low;@wilhelm2016rpa;@luenser2017;@graf2018accurate;@duchemin2019separable;@drontschenko2022efficient;@kutepov2012electronic;@kutepov2017linearized].
 
 An important consideration for low-scaling algorithms is the crossover point. Due to their larger pre-factor, low-scaling algorithms are typically more expensive for smaller systems and only become more cost effective than canonical implementations for larger systems due to their reduced scaling [@wilhelm2018toward]. Furthermore, the numerical precision of low-scaling \textit{GW} algorithms is strongly coupled to the time-frequency treatment  [@wilhelm2021low]. Early low-scaling \textit{GW} algorithms did not reach the same precision as canonical implementations [@vlcek2017stochastic; @wilhelm2018toward; @forster2020low]. Although appropriate Fourier transforms and corresponding time-frequency grids have been implemented [@liu2016cubic;@wilhelm2021low; @duchemin2021cubic; @foerster2021GW100], these implementations and grids are tied to particular codes and are often buried deeply inside the code. Furthermore, reuse of such implementations elsewhere is often restricted by license requirements or dependencies on definitions made in the host code.
 

JOSS/refs.bib

@@ -742,3 +742,13 @@ @article{kutepov2017linearized
   doi = {10.1016/j.cpc.2017.06.012},
   publisher={Elsevier}
 }
+@article{Graml2023,
+  	Archiveprefix = {arXiv},
+    author={Maximilian Graml and Klaus Zollner and Daniel Hernangómez-Pérez and Faria Junior, Paulo E.  and Jan Wilhelm},
+    eprint={2306.16066},
+  	Journal = {ArXiv e-prints},
+  	Month = jul,
+    doi = {10.48550/arXiv.2306.16066},
+  	Title = {{Low-scaling GW algorithm applied to twisted transition-metal dichalcogenide heterobilayers}},
+  	Year = 2023
+}