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@ 77,6 +77,8 @@ To clarify this point, we have added a sentence to the paper:


finite projecting time are smaller than the error bars.''


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%%% REVIEWER 2 %%%


\textbf{\large Reviewer \#2}


@ 103,139 +105,12 @@ We have shown that this problem can be alleviated with the hereproposed method


We believe that applying the RSDFTCIPSI scheme to strongly


correlated systems is indeed an interesting topic, but it clearly goes


beyond the scope of the present manuscript.


Consequently, we prefer to leave the study RSDFTCIPSI trial wave functions on strongly correlated systems for a future study.


Consequently, we prefer to leave the study of RSDFTCIPSI trial wave functions on strongly correlated systems for a future work.


This has been mentioned in the concluding section of the revised manuscript.


}








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\begin{document}




\begin{letter}%


{To the Editors of the Journal of Chemical Physics}




\opening{Dear Editors,}




\justifying


Please find attached a revised version of the manuscript entitled


{\it ``Taming the fixednode error in diffusion Monte Carlo via range separation''}.


We would like to thank the reviewers for their constructive comments.


Our detailed responses to their comments can be found below.


For convenience, all modifications and changes are highlighted in red in the revised version of the manuscript.


We hope that you will agree that our manuscript is now suitable for publication in JCP.




We look forward to hearing from you.




\closing{Sincerely, the authors.}




\newpage




%%% REVIEWER 1 %%%


\noindent \textbf{\large Reviewer \#1}




It is assumed that the nonvariational mixed estimator is used for the


FNDMC energy. How adequate is the discussion on the error using a


lower energy in this case? Please elaborate this in detail.




\alert{\textbf{Response:}


The nonvariational mixed estimator is not used for the FNDMC energy


in this work.


We have used the variant of Assaraf, Caffarel and


Khelif\cite{Assaraf_2000} (ref 112 in the paper) of the Stochastic


Reconfiguration (SR) algorithm developped by Hetherington and


Sorella.\cite{Sorella_Hetherington_1984,1998,Sorella_2000}


It is smart algorithm mixing pure diffusion Monte Carlo (PDMC) and DMC


and taking the best of those 2 methods~: the DMC algorithm is stable


at the cost of the introduction of a finite population bias, and the


PDMC algorithm is stabilized by introducing a finite projecting time.


The SR algorithm has 2 limits: with a single walker it falls back to


PDMC, and with an infinite population the DMC is recovered. The mixing


of the 2 methods does not introduce the population control bias of


DMC, and requires a much shorter projecting time than PDMC. In


practice, it is quite easy to reach a regime where the number of


walkers and the projecting time are such that the simulation is


stable, the bias due to the finite projecting time is negligible and


the fluctuations introduced by the projection are small.


}




\alert{


To clarify this point, we have added a sentence to the paper:


\quote{


With such parameters, both the timestep error and the bias due to the


finite projecting time are smaller than the error bars.


}


}






%%% REVIEWER 2 %%%


\textbf{\large Reviewer \#2}






The only criticism I have is about the examples reported. Despite the


importance of the G1 test set, for which the atomization energies have


been computed, I would like to see an example where the ground state


has a true multireference character. Indeed, as the authors pointed out,


the G1 set is only weakly correlated, and RSDFTCIPSI does not show its


best performances, and does not pay off. Indeed, in the G1 set, basisset


effects on the nodal surface quality seem to be more important than the


effect of dealing with a multireference wave function.






\alert{\textbf{Response:}


We totally agree with the reviewer, CIPSI trial wave functions can


handle very well multiconfigurational effects. In cases such as


the G1 set, although the total FNDMC energies are extremely low the


energy differences are difficult to control, especially for large


systems. This was a limit of the use of CIPSI wave functions for


QMC. Here, we have shown that this gap can be filled with the proposed


method. We believe that using RSDFTCIPSI in the context of strongly


correlated systems is indeed an interesting topic, but it goes a bit


beyond the scope of the present manuscript. Of course, we intend to


study RSDFTCIPSI trial wave functions on strongly correlated systems


in a near future.


}








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\bibliography{ResponseLetter}


\end{letter}


\end{document}





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