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dft_tools/doc/guide/dftdmft_selfcons.rst
Priyanka Seth 8dc42b08ae [doc] New documentation
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.. _full_charge_selfcons:
Full charge self consistency
============================
Wien2k + dmftproj
-----------------
.. warning::
TO BE UPDATED!
.. warning::
Before using this tool, you should be familiar with the band-structure package :program:`Wien2k`, since
the calculation is controlled by the :program:`Wien2k` scripts! See also the :download:`dmftproj tutorial<images_scripts/TutorialDmftproj.pdf>`.
In order to do charge self-consistent calculations, we have to tell the band structure program about the
changes in the charge density due to correlation effects. In the following, we discuss how to use the
:program:`TRIQS` tools in combination with the :program:`Wien2k` program, although an extension to other
codes is also possible.
We can use the DMFT script as introduced in sections :ref:`DFTDMFTmain` and :ref:`advanced`, with a few simple
modifications. First, in order to be compatible with the :program:`Wien2k` standards, the DMFT script has to be
named ``case.py``, where `case` is the name of the :program:`Wien2k` calculation, see the section
:ref:`interfacetowien` for details. We can then set the variable `dft_filename` dynamically::
import os
dft_filename = os.getcwd().rpartition('/')[2]
This sets the `dft_filename` to the name of the current directory. The remainder of the script is identical to
that for one-shot calculations. Only at the very end do we have to calculate the modified charge density,
and store it in a format such that :program:`Wien2k` can read it. Therefore, after the DMFT loop that we saw in the
previous section, we symmetrise the self energy, and recalculate the impurity Green function::
SK.symm_deg_gf(S.Sigma,orb=0)
S.G_iw << inverse(S.G0_iw) - S.Sigma_iw
S.G_iw.invert()
These steps are not necessary, but can help to reduce fluctuations in the total energy.
Now we calculate the modified charge density::
# find exact chemical potential
SK.put_Sigma(Sigma_imp = [ S.Sigma_iw ])
chemical_potential = SK.calc_mu( precision = 0.000001 )
dN, d = SK.calc_density_correction(filename = dft_filename+'.qdmft')
SK.save(['chemical_potential','dc_imp','dc_energ'])
First we find the chemical potential with high precision, and after that the routine
``SK.calc_density_correction(filename)`` calculates the density matrix including correlation effects. The result
is stored in the file `dft_filename.qdmft`, which is later read by the :program:`Wien2k` program. The last statement saves
the chemical potential into the hdf5 archive.
We need also the correlation energy, which we evaluate by the Migdal formula::
correnerg = 0.5 * (S.G_iw * S.Sigma_iw).total_density()
From this value, we substract the double counting energy::
correnerg -= SK.dc_energ[0]
and save this value too::
if (mpi.is_master_node()):
f=open(dft_filename+'.qdmft','a')
f.write("%.16f\n"%correnerg)
f.close()
The above steps are valid for a calculation with only one correlated atom in the unit cell, the most likely case
where you will apply this method. That is the reason why we give the index `0` in the list `SK.dc_energ`.
If you have more than one correlated atom in the unit cell, but all of them
are equivalent atoms, you have to multiply the `correnerg` by their multiplicity before writing it to the file.
The multiplicity is easily found in the main input file of the :program:`Wien2k` package, i.e. `case.struct`. In case of
non-equivalent atoms, the correlation energy has to be calculated for all of them separately (FOR EXPERTS ONLY).
As mentioned above, the calculation is controlled by the :program:`Wien2k` scripts and not by :program:`python`
routines. Therefore, at the command line, you start your calculation for instance by::
me@home $ run -qdmft -i 10
The flag `-qdmft` tells the script that the density matrix including correlation effects is to be read in from the `case.qdmft`
file and that 10 self-consistency iterations are to be done. If you run the code on a parallel machine, you can specify the number of
nodes to be used with the `-np` flag::
me@home $ run -qdmft -np 64 -i 10
In that case, you have to give the proper `MPI` execution statement, e.g. `mpiexec`, in the `run_lapw` script (see the
corresponding :program:`Wien2k` documentation). In many cases it is advisable to start from a converged one-shot
calculation.
For practical purposes, you keep the number of DMFT loops within one DFT cycle low, or even to `loops=1`. If you encouter
unstable convergence, you have to adjust the parameters such as
`loops`, `mix`, or `Delta_mix` to improve the convergence.
In the next section, :ref:`DFTDMFTtutorial`, we will see in a detailed
example how such a self consistent calculation is performed.
VASP + wannier90
----------------
.. warning::
IN PROGRESS!