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LRTDDFT of excited state
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1:05:06 PM PST  Wed, Dec 5th 2018 

Dear all,
I am trying to calculate the absorption spectrum of excited CHBr3 with an extended CBr bond. CHBr3 is in excited T2, where one HOMO1 electron is excited to LUMO. I can use the following tag to do it, however, it doesn't necessarily converge to T2 state. Sometimes, it only converges to T1 state.
vectors swap beta 55 56
MAX_OVL
Is it possible to run a first LRTDDFT calculation with tag CIVECS to export the T2 state and perform the second LRTDDFT based on the CI vectors from the first step?
If we have to revise the code, what subroutines I should revise?
Thanks a lot!




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5:50:32 AM PST  Fri, Dec 7th 2018 

No, you cannot perform a second LRTDDFT calculation with a reference built from the CI vectors of an excited state. LRTDDFT is formulated for a single determinant reference state and your T2 solution from the first LRTDDFT would be a linear combination of determinants.
Quadratic response TDDFT could give you the absorption spectrum for an excited state; however, it is not implemented in NWChem (the only implementation I am aware of is in the code Dalton).
Within NWChem, you could attempt our RTTDDFT based approach to excited state absorption ("Excited state absorption from realtime timedependent density functional theory" JCTC vol. 11 42944303 [2015] https://pubs.acs.org/doi/abs/10.1021/acs.jctc.5b00473). While not documented, the functionality for this approach is in version 6.8 of the code. You would need to first run a LRTDDFT gradient calculation on the excited state of interest in order to generate the excited state density matrix (or matrices in the case of an open shell calculation). You can then use the excited state density as your starting point by adding the appropriate load command to your RTTDDFT input block:
load density <file name>.dmat
for closed shell or
load density <file name>.dmatA <file name>.dmatB
for open shell, where you would obviously insert the file name for the density matrix (matrices) generated from the LRTDDFT gradient calculation. From there you should be able to follow the method outlined in the above cited paper to generate the absorption spectrum.

Edited On 5:52:34 AM PST  Fri, Dec 7th 2018 by Sean




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9:36:21 AM PST  Thu, Jan 31st 2019 

Hi..in my case i was trying to do excited state geometry optimization using the analytical gradient of LRTDDFT. I did not find the relevant part of the manual that describes the gradient calculation of LRTDDFT. so, I am wondering if this function available in dalton? if yes, how to use it? Thank you for your help!
https://www.7pcb.com/

Edited On 4:09:18 PM PST  Thu, Feb 28th 2019 by AvritSase




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9:29:02 AM PST  Tue, Feb 5th 2019 

Sorry I misunderstood you. Geometry optimization on the excited state is straightforward within NWChem. See the third example input in the documentation https://github.com/nwchemgit/nwchem/wiki/ExcitedStateCalculations#sampleinput




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6:00:25 AM PST  Wed, Nov 20th 2019 

Quote:Sean Dec 7th 4:50 amNo, you cannot perform a second LRTDDFT calculation with a reference built from the CI vectors of an excited state. LRTDDFT is formulated for a single determinant reference state and your T2 solution from the first LRTDDFT would be a linear combination of determinants.
Quadratic response TDDFT could give you the absorption spectrum for an excited state; however, it is not implemented in NWChem (the only implementation I am aware of is in the code Dalton).
Within NWChem, you could attempt our RTTDDFT based approach to excited state absorption ("Excited state absorption from realtime timedependent density functional theory" JCTC vol. 11 42944303 [2015] https://pubs.acs.org/doi/abs/10.1021/acs.jctc.5b00473). While not documented, the functionality for this approach is in version 6.8 of the code. You would need to first run a LRTDDFT gradient calculation on the excited state of interest in order to generate the excited state density matrix (or matrices in the case of an open shell calculation). You can then use the excited state density as your starting point by adding the appropriate load command to your RTTDDFT input block:
load density <file name>.dmat
for closed shell or
load density <file name>.dmatA <file name>.dmatB
for open shell, where you would obviously insert the file name for the density matrix (matrices) generated from the LRTDDFT gradient calculation. From there you should be able to follow the method outlined in the above cited paper to generate the absorption spectrum.
Hi Sean,
Could you please elaborate a bit on the RTTDDFT with an example input?
Also once the ESA spectrum is obtained, can you give an example input on how to plot the RTTDHF transition densities for each of the features of the spectrum?
Thanks in advance




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9:26:29 AM PST  Thu, Nov 21st 2019 

Below is an example for calculating the excited state response with RTTDDFT for the 5th excited state of a water molecule. Some items to note:
A LRTDDFT gradient calculation needs to be performed first in order to generate the excited state density matrix
That density matrix needs to be loaded within the RTTDDFT block of the input
In addition to the RTTDDFT runs with field applied (here I have only done the excitation along the xaxis), an additional run needs to be done without any field applied; this extra run is used to correct the dipole moments from the runs with an applied field
Obtaining transition densities to plot is significantly more involved than just the spectrum. You need to add a visualization block to each RTTDDFT input block. This will generate a density matrix file for each step in each RTTDDFT run. Then as with the dipole moment, the run without a field is used to correct the runs with a field. In the examples that we showed in the paper, the probed transitions were polarized along a single axis so we just used the density matrices from the run with a field along that axis. After a series of corrected density matrices are generated, then you need to take the Fourier transform of the density matrices at the transition frequency. The resulting density matrix can then be fed into DPLOT to generate a cube file for visualization.
echo
start water
geometry "system" units angstroms nocenter noautoz noautosym
O 0.00000000 0.00000000 0.15104872
H 0.75808206 0.00000000 0.48477224
H 0.75808206 0.00000000 0.48477224
end
set geometry "system"
basis
* library STO3G
end
dft
xc hfexch 1.0
end
tddft
nroots 10
notriplet
civecs
grad
root 5
end
end
task tddft gradient
unset rt_tddft:*
rt_tddft
tmax 200.0
dt 0.2
load density water.dmat
tag "ref"
print dipole
end
task dft rt_tddft
unset rt_tddft:*
rt_tddft
tmax 200.0
dt 0.2
load density water.dmat
tag "kick_x"
print dipole
field "kick"
type delta
polarization x
max 0.0001
end
excite "system" with "kick"
end
task dft rt_tddft




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3:53:36 AM PST  Fri, Nov 22nd 2019 

Thanks for your prompt reply Sean,
Do I need to add this additional nofield step at every polarization x, y and z?
Would the method work after a tddft optimization such as below?
tddft
nroots 10
notriplet
civecs
target 5
targetsym a
grad
root 5
end
end
task tddft optimize
What is the difference between the two calculations? Does the gradient calculation produce a spectrum near the vertical excitation while the other at the equilibrated state?
As for the visualization, would something like this work:
unset rt_tddft:*
rt_tddft
tmax 200.0
dt 0.2
load density water.dmat
tag "ref"
print dipole
end
task dft rt_tddft
unset rt_tddft:*
rt_tddft
tmax 200.0
dt 0.2
load density water.dmat
tag "kick_x"
print dipole
field "kick"
type delta
polarization x
max 0.0001
end
excite "system" with "kick"
end
visualization
tstart 0.0
tend 200.0
treference 0.0
dplot
end
task dft rt_tddft
What FFT utility would you use for the fourier transform?
Thanks for all your help




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11:51:09 AM PST  Fri, Nov 22nd 2019 

You only need to run the calculation without a field once.
Running an optimization first would be fine. My example input assumed you already had the geometry of interest. The reason it was set as a call to tddft gradient is that the excited state density matrix is not computed if you are only calculating the excitation energies; it is only calculated if you need an excited state gradient. If you are doing a geometry optimization on the excited state, then you will be calculating excited state gradients and will be generating the excited state density matrix. The caveat to this is that the XC functional must have analytical 2nd derivatives implemented; otherwise, the code is calculating gradients through numerical differentiation and an excited state density matrix is not calculated. For those functionals (e.g. M06), we showed that one possible workaround is to use a different functional to calculated the excited state density matrix and then use the functional of interest for the RTTDDFT propagation (see, https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.6b00282)
You need a visualization block for each propagation, including the one without a field. Also, don't include the treference keyword or the dplot keyword.
Use whatever FFT utility works for you. In the past I wrote analysis code in Fortran around FFTPACK from netlib, but most programing languages have FFT libraries that can be linked.




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6:36:40 AM PST  Sat, Nov 23rd 2019 

Once the calculaton is finished, how would you amend these commands to include the reference without the field?
Extracting the xdipole moment for the xkick:
nw_rtparse.py xdipole px tkick_x input.nwo > x.dat
FT for x polarization:
fft1d d50 z p50000 < x.dat  rotate_fft > xw.dat




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6:47:56 AM PST  Mon, Nov 25th 2019 

One possibility is
run:
nw_rtparse.py xdipole px tkick_x input.nwo > x.dat
then:
nw_rtparse.py xdipole px tref input.nwo > refx.dat
then:
paste x.dat refx.dat  tail n <number of data points>  awk '{print $1,$2$4}' > corrected_x.dat
The top of the x.dat/refx.dat files should tell you how many data points you have. You should then be able to feed the corrected_x.dat file into the fft1d utility. You can repeat the same for the other polarizations, e.g.
run:
nw_rtparse.py xdipole py tkick_y input.nwo > y.dat
then:
nw_rtparse.py xdipole py tref input.nwo > refy.dat
then:
paste y.dat refy.dat  tail n <number of data points>  awk '{print $1,$2$4}' > corrected_y.dat
The point is just that you want to subtract the nofield propagated dipole from the field propagated dipole at each time step before doing the FT.




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5:55:25 AM PST  Tue, Nov 26th 2019 

Thank you Sean for the clear explanation. Meanwhile I have some troubles with the rotate_fft.py script:
I cannot use it together with fft1d.m as suggested in the H20 example. The command below,
octave q fft1d.m < x.dat  rotate_fft.py > xw.dat
produces the error:
error: fft1d.m: A(I): index out of bounds; value 1 out of bound 0
error: called from
fft1d.m at line 41 column 7
While octave q fft1d.m x.dat > xw.dat works fine, and trying to do the rotate on the resulting file xw,dat also does not work
rotate_fft.py xw.dat > xw_rot.dat




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12:38:06 PM PST  Tue, Nov 26th 2019 

Let me begin by saying that the scripts in $NWCHEM_TOP/contrib/parsers are not mine, but I think I have managed to get them to work.
As you have already discovered, the fft1d.m utility takes the input as a command line argument rather than a redirect from stdin. Also note that the documentation for RTTDDFT says that there are a couple parameters hardcoded into that utility, specifically, the time constant for the exponential damping of the signal and the amount of zeropadding. You may want to considering playing around with those numbers to improve the appearance of your output.
To use the rotate_fft.py script on the output of the fft1d.m utility, you need to first delete the header of that xw.dat file (i.e. delete all the text at the top so that the file only contains the 4 columns of numbers. Then rotate_fft.py takes its input from a redirect rather than the command line, i.e. rotate_fft.py < xw.dat (alternatively, python rotate_fft.py < xw.dat, if you haven't made rotate_fft.py executable).




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8:01:26 AM PST  Wed, Nov 27th 2019 

Thanks Sean rotate_fft works now with the redirect.
I now encountered a problem when using damping values >5.0 and for the first points of my data. rotate_fft.py fails with the exception:
("abs not equal to sqrt(re^2 + im^2)")
In order for rotate_fft to run through all the data, I have to either discard the first ~300 points or loosen the difference between Abs and the sum of square roots of Im and Re to < 1e5 instead of the default < 1e6 in rotate_fft.py. By adding more damping the error is increased and (Abs  sqrt(Im^2+ Re^2) has to be loosened further.
In your experience, do you think this is normal? What does this say about the quality of the data?




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9:58:15 AM PST  Mon, Dec 2nd 2019 

This is not normal. That part of the rotate_fft script is just checking the data that you are inputing, so if you are getting that error message, something has gone wrong with the previous steps. Have you looked at your timedependent dipole moment to check that it looks reasonable? Have you looked at the output of fft1d.m to see if that looks reasonable? For the example input I provided above, I am able to run both fft1d.m and rotate_fft.py without issue, have you been able to do the same?



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