modifs T2
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@ 55,9 +55,9 @@ 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.


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_1998,Hetherington_1984,Sorella_2000}


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


reconfiguration (SR) algorithm developped by Hetherington and


Sorella \cite{Sorella_1998,Hetherington_1984,Sorella_2000}.


It is an algorithm mixing pure diffusion Monte Carlo (PDMC) with DMC, such that


the mixing does not introduce the population control bias of DMC, and requires a


much shorter projecting time than PDMC.


@ 67,7 +67,7 @@ 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.


So the nonvariational mixed estimator is not used for the FNDMC energy


So the nonvariational mixed estimator has not been used for the FNDMC energy


in this work.


}




@ 93,18 +93,18 @@ effect of dealing with a multireference wave function.






\alert{\textbf{Response:}


We totally agree with the reviewer that this method would perform even better


with strongly correlated systems. But in cases such as


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


trial wave functions, the energy differences are difficult to control. This is


even more true when the systems become large, and


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 applying the RSDFTCIPSI to strongly


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


beyond the scope of the present manuscript and we prefer to leave the


study RSDFTCIPSI trial wave functions on strongly correlated systems


for a next study.


We agree with the reviewer that the present method would perform even better


with strongly correlated systems. However, for systems such as


the ones gathered in the G1 set, although the total FNDMC energies are extremely low with CIPSI


trial wave functions, energy differences are difficult to control.


This comment is also valid when systems get large, and


this was a clear limitation of the use of CIPSI trial wave functions within QMC.


We have shown that this problem can be alleviated with the hereproposed method which combines RSDFT and CIPSI.


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.


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


}







@ 992,6 +992,8 @@ value of $\mu$ can be further reduced to $0.25$~bohr$^{1}$ to get


extremely compact wave functions at the price of less efficient


cancellations of errors.




\alert{We hope to report, in the near future, a detailed investigation of stronglycorrelated systems with the present RSDFTCIPSI scheme.}




%%%%%%%%%%%%%%%%%%%%%%%%


\begin{acknowledgments}


A.B was supported by the U.S.~Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials.



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