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* Adapt the AC parts to the new TRIQS/maxent package * Restructure the explaination on how to run the scripts * Corrections of many typos and pytriqs occurences.
153 lines
7.1 KiB
ReStructuredText
153 lines
7.1 KiB
ReStructuredText
.. _full_charge_selfcons:
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Full charge self-consistency
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============================
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In order to do charge self-consistent calculations, we have to tell the band structure program about the
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changes in the charge density due to correlation effects. The feedback of the charge density is generally
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program-dependent and the procedure for running charge self-consistent calculations has to be adopted
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accordingly for a given band structure program. Below we describe two implementations based on Wien2k
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and VASP codes.
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Wien2k + dmftproj
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-----------------
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.. warning::
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Before using this tool, you should be familiar with the band-structure package Wien2k, since
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the calculation is controlled by the Wien2k scripts! Be
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sure that you also understand how :program:`dmftproj` is used to
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construct the Wannier functions. For this step, see either sections
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:ref:`conversion`, or the extensive :download:`dmftproj manual<images_scripts/TutorialDmftproj.pdf>`.
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In the following, we discuss how to use the
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:ref:`TRIQS <triqslibs:installation>` tools in combination with the Wien2k program.
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We can use the DMFT script as introduced in section :ref:`singleshot`,
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with just a few simple modifications. First, in order to be compatible with the Wien2k standards,
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the DMFT script has to be named :file:`case.py`, where `case` is the place holder name of the Wien2k
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calculation, see the section :ref:`conversion` for details. We can then set the variable `dft_filename` dynamically::
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import os
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dft_filename = os.getcwd().rpartition('/')[2]
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This sets the `dft_filename` to the name of the current directory. The
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remaining part of the script is identical to
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that for one-shot calculations. Only at the very end we have to calculate the modified charge density,
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and store it in a format such that Wien2k can read it. Therefore, after the DMFT loop that we saw in the
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previous section, we symmetrise the self energy, and recalculate the impurity Green function::
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SK.symm_deg_gf(S.Sigma,orb=0)
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S.G_iw << inverse(S.G0_iw) - S.Sigma_iw
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S.G_iw.invert()
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These steps are not necessary, but can help to reduce fluctuations in the total energy.
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Now we calculate the modified charge density::
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# find exact chemical potential
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SK.set_Sigma([ S.Sigma_iw ])
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chemical_potential = SK.calc_mu( precision = 0.000001 )
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dN, d = SK.calc_density_correction(filename = dft_filename+'.qdmft')
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SK.save(['chemical_potential','dc_imp','dc_energ'])
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First we find the chemical potential with high precision, and after that the routine
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``SK.calc_density_correction(filename)`` calculates the density matrix including correlation effects. The result
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is stored in the file `dft_filename.qdmft`, which is later read by the Wien2k program. The last statement saves
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the chemical potential into the hdf5 archive.
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We need also the correlation energy, which we evaluate by the Migdal formula::
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correnerg = 0.5 * (S.G_iw * S.Sigma_iw).total_density()
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Other ways of calculating the correlation energy are possible, for
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instance a direct measurement of the expectation value of the
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interacting Hamiltonian. However, the Migdal formula works always,
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independent of the solver that is used to solve the impurity problem.
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From this value, we subtract the double counting energy::
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correnerg -= SK.dc_energ[0]
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and save this value in the file, too::
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if (mpi.is_master_node()):
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f=open(dft_filename+'.qdmft','a')
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f.write("%.16f\n"%correnerg)
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f.close()
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The above steps are valid for a calculation with only one correlated atom in the unit cell, the most likely case
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where you will apply this method. That is the reason why we give the index `0` in the list `SK.dc_energ`.
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If you have more than one correlated atom in the unit cell, but all of them
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are equivalent atoms, you have to multiply the `correnerg` by their multiplicity before writing it to the file.
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The multiplicity is easily found in the main input file of the Wien2k package, i.e. `case.struct`. In case of
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non-equivalent atoms, the correlation energy has to be calculated for
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all of them separately and summed up.
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As mentioned above, the calculation is controlled by the Wien2k scripts and not by :program:`python`
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routines. You should think of replacing the lapw2 part of the Wien2k self-consistency cycle by
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| `lapw2 -almd`
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| `dmftproj`
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| `python case.py`
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| `lapw2 -qdmft`
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In other words, for the calculation of the density matrix in lapw2, we
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add the DMFT corrections through our python scripts.
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Therefore, at the command line, you start your calculation for instance by:
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`me@home $ run -qdmft 1 -i 10`
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The flag `-qdmft` tells the Wien2k script that the density
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matrix including correlation effects is to be read in from the
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`case.qdmft` file, and that you want the code to run on one computing
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core only. Moreover, we ask for 10 self-consistency iterations are to be
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done. If you run the code on a parallel machine, you can specify the
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number of nodes to be used:
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`me@home $ run -qdmft 64 -i 10`
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In that case, you will run on 64 computing cores. As standard setting,
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we use `mpirun` as the proper MPI execution statement. If you happen
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to have a different, non-standard MPI setup, you have to give the
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proper MPI execution statement, in the `run_lapw` script (see the
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corresponding Wien2k documentation).
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In many cases it is advisable to start from a converged one-shot
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calculation. For practical purposes, you keep the number of DMFT loops
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within one DFT cycle low, or even to `loops=1`. If you encounter
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unstable convergence, you have to adjust the parameters such as
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the number of DMFT loops, or some mixing of the self energy to improve
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the convergence.
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In the section :ref:`DFTDMFTtutorial` we will see in a detailed
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example how such a self-consistent calculation is performed from scratch.
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VASP + PLOVasp
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--------------
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.. warning::
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This is a preliminary documentation valid for the alpha-version of the interface.
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Modifications to the implementation might be introduced before the final release.
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Unlike Wien2k implementation the charge self-consistent DMFT cycle
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in the framework of PLOVasp interface is controlled by an external script.
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Because of the specific way the DFT self-consistency is implemented in VASP
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the latter has to run parallel to the DMFT script, with the synchronisation being
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ensured by a lock file. PLOVasp interface provides a shell-script :program:`vasp_dmft.sh`
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which takes care of the process management. The user must, however, specify a path
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to VASP code and provide the DMFT Python-script.
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The user-provided script is almost the same as for Wien2k charge self-consistent
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calculations with the main difference that its functionality (apart from
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the lines importing other modules) should be placed inside a function `dmft_cycle()`
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which will be called every DMFT cycle. Another difference is the way
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function `calc_density_correction()` works.
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Other DFT codes
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---------------
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The extension to other DFT codes is straight forward. As described
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here, one needs to implement the correlated density matrix to be used
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for the calculation of the charge density. This implementation will of
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course depend on the DFT package, and might be easy to do or a quite
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involved project. The formalism, however, is straight forward.
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