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@ -636,7 +636,7 @@ Note that, after this transformation, the form of the regularizer is actually cl
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% Reference comp det
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% Reference comp det
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Our set is composed by 50 closed-shell organic molecules, displayed in Fig.~\ref{fig:mol}, with singlet ground states.
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Our set is composed by closed-shell organic molecules that correspond to the 50 smallest atoms and molecules of the $GW$100 benchmark set. \cite{vanSetten_2015}
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Following the same philosophy as the \textsc{quest} database for neutral excited states, \cite{Loos_2020d,Veril_2021} their geometries have been optimized at the CC3 level \cite{Christiansen_1995b,Koch_1997} in the aug-cc-pVTZ basis set using the \textsc{cfour} program. \cite{CFOUR}
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Following the same philosophy as the \textsc{quest} database for neutral excited states, \cite{Loos_2020d,Veril_2021} their geometries have been optimized at the CC3 level \cite{Christiansen_1995b,Koch_1997} in the aug-cc-pVTZ basis set using the \textsc{cfour} program. \cite{CFOUR}
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The reference CCSD(T) principal ionization potentials (IPs) and electron affinities (EAs) have been obtained using Gaussian 16 \cite{g16} with default parameters, that is, within the restricted and unrestriced HF formalism for the neutral and charged species, respectively.
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The reference CCSD(T) principal ionization potentials (IPs) and electron affinities (EAs) have been obtained using Gaussian 16 \cite{g16} with default parameters, that is, within the restricted and unrestriced HF formalism for the neutral and charged species, respectively.
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@ -648,6 +648,7 @@ We use (restricted) HF guess orbitals and energies for all self-consistent $GW$
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The maximum size of the DIIS space \cite{Pulay_1980,Pulay_1982} and the maximum number of iterations were set to 5 and 64, respectively.
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The maximum size of the DIIS space \cite{Pulay_1980,Pulay_1982} and the maximum number of iterations were set to 5 and 64, respectively.
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In practice, one may achieve convergence, in some cases, by adjusting these parameters or by using an alternative mixing scheme.
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In practice, one may achieve convergence, in some cases, by adjusting these parameters or by using an alternative mixing scheme.
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However, in order to perform a black-box comparison, these parameters have been fixed to these default values.
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However, in order to perform a black-box comparison, these parameters have been fixed to these default values.
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\titou{The $\eta$ value used in the convetional $G_0W_0$ and $qsGW$ calculations corresponds to the largest value where one succesfully converges all systems.}
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\section{Results}
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\section{Results}
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@ -821,20 +822,18 @@ Therefore, it seems that the effect of the TDA cannot be systematically predicte
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\end{figure*}
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\end{figure*}
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%%% %%% %%% %%%
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%%% %%% %%% %%%
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The test set considered in this study is made of the 50 smallest atoms and molecules of the $GW$100 benchmark set. \cite{vanSetten_2015}
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%This set has been augmented with the MgO and O3 molecules (which are part of GW100 as well) because they are known to possess intruder states. \cite{vanSetten_2015,Forster_2021}
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Table \ref{tab:tab1} shows the principal IP of the 50 molecules considered in this work computed at various levels of theory.
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Table \ref{tab:tab1} shows the principal IP of the 50 molecules considered in this work computed at various levels of theory.
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As mentioned previously the HF IPs are overestimated with a mean signed error (MSE) of \SI{0.64}{\electronvolt} and a mean absolute error (MAE) of \SI{0.74}{\electronvolt}.
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As mentioned previously the HF approximation overestimates the IPs with a mean signed error (MSE) of \SI{0.64}{\eV} and a mean absolute error (MAE) of \SI{0.74}{\eV}.
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Performing a one-shot $G_0W_0$ calculation on top these mean-field results allows to divided by more than two the MSE and MAE, \SI{0.26}{\electronvolt} and \SI{0.32}{\electronvolt}, respectively.
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Performing a one-shot $G_0W_0$ calculation on top of this mean-field starting point, $G_0W_0$@HF, reduces by more than a factor two the MSE and MAE, \SI{0.26}{\eV} and \SI{0.32}{\eV}, respectively.
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However, there are still outliers with quite large errors, for example the IP of the dinitrogen is overestimated by \SI{1.56}{\electronvolt}.
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However, there are still outliers with large errors.
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Self-consistency can mitigate the error of the outliers as the maximum error at the qs$GW$ level is now \SI{0.56}{\electronvolt} and the standard deviation error (SDE) is decreased from \SI{0.39}{\electronvolt} for $G_0W_0$ to \SI{0.18}{\electronvolt} for qs$GW$.
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For example, the IP of \ce{N2} is overestimated by \SI{1.56}{\eV}.
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In addition, the MSE and MAE (\SI{0.24}{\electronvolt}/\SI{0.25}{\electronvolt}) are also slightly improved with respect to $G_0W_0$@HF.
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Self-consistency can mitigate the error of the outliers as the maximum error at the qs$GW$ level is now \SI{0.56}{\eV} and the standard deviation of the error (SDE) is decreased from \SI{0.39}{\eV} for $G_0W_0$ to \SI{0.18}{\eV} for qs$GW$.
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In addition, the MSE and MAE (\SI{0.24}{\eV} and \SI{0.25}{\eV}, respectively) are also slightly improved with respect to $G_0W_0$@HF.
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Now turning to the new results of this manuscript, \ie the alternative self-consistent scheme SRG-qs$GW$.
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Now turning to the new results of this manuscript, \ie the alternative self-consistent scheme SRG-qs$GW$.
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Table~\ref{tab:tab1} shows the SRG-qs$GW$ values for $s=100$.
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Table \ref{tab:tab1} shows the SRG-qs$GW$ values for $s=100$.
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The statistical descriptors corresponding to the alternative static self-energy are all improved with respect to qs$GW$.
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The statistical descriptors corresponding to this alternative static self-energy are all improved with respect to qs$GW$.
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Of course these are slight improvements but this is done with no additional computational cost and can be implemented really quickly just by changing the form of the static approximation.
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Of course, these are slight improvements but this is done with no additional computational cost and can be implemented really easily by changing the form of the static approximation.
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The evolution of the statistical descriptors with respect to the various methods considered in Table~\ref{tab:tab1} is graphically illustrated by Fig.~\ref{fig:fig4}.
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The evolution of the statistical descriptors with respect to the various methods considered in Table~\ref{tab:tab1} is graphically illustrated by Fig.~\ref{fig:fig4}.
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The decrease of the MSE and SDE correspond to a shift of the maximum toward zero and a shrink of the distribution width, respectively.
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The decrease of the MSE and SDE correspond to a shift of the maximum toward zero and a shrink of the distribution width, respectively.
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