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55
doc/Ce-HI.rst
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@ -51,7 +51,7 @@ specify the energy window for Wannier functions' construction. For a more comple
To prepaire input data for :program:`dmftproj` we execute :program:`lapw2` with the `-almd` option ::
lapw2 -almd
x lapw2 -almd
Then :program:`dmftproj` is executed in its default mode (i.e. without spin-polarization or spin-orbit included) ::
@ -94,18 +94,29 @@ where the solver is initialized with the value of `beta`, and the orbital quantu
The Hubbard-I initialization `Solver` has also optional parameters one may use:
* `n_msb`: is the number of Matsubara frequencies used (default is `n_msb=1025`)
* `use_spin_orbit`: if set 'True' the solver is run with spin-orbit coupling included. To perform actual LDA+DMFT calculations with spin-orbit one should also run :program:`Wien2k` and :program:`dmftproj` in spin-polarized mode and with spin-orbit included. By default `use_spin_orbit=False`
* `n_msb`: the number of Matsubara frequencies used. The default is `n_msb=1025`.
* `use_spin_orbit`: if set 'True' the solver is run with spin-orbit coupling
included. To perform actual LDA+DMFT calculations with spin-orbit one should
also run :program:`Wien2k` and :program:`dmftproj` in spin-polarized mode and
with spin-orbit included. By default, `use_spin_orbit=False`.
The `Solver.solve(U_int, J_hund)` statement has two necessary parameters, the `U` parameter (`U_int`) and Hund's rule coupling `J_hund`. Notice that the solver constructs the full 4-index `U`-matrix by default, and the `U` parameter is in fact the Slatter `F0` integral. Other optional parameters are:
The `Solver.solve(U_int, J_hund)` statement has two necessary parameters, the
Hubbard U parameter `U_int` and Hund's rule coupling `J_hund`. Notice that the
solver constructs the full 4-index `U`-matrix by default, and the `U_int` parameter
is in fact the Slatter `F0` integral. Other optional parameters are:
* `T`: A matrix that transforms the interaction matrix from complex spherical harmonics to a symmetry adapted basis. By default complex spherical harmonics basis is used and `T=None`
* `verbosity` tunes output from the solver. If `verbosity=0` only basic information is printed, if `verbosity=1` the ground state atomic occupancy and its energy are printed, if `verbosity=2` additional information is printed for all occupancies that were diagonalized. By default `verbosity=0`
* `T`: matrix that transforms the interaction matrix from complex spherical
harmonics to a symmetry adapted basis. By default, the complex spherical harmonics
basis is used and `T=None`.
* `verbosity`: tunes output from the solver. If `verbosity=0` only basic
information is printed, if `verbosity=1` the ground state atomic occupancy and
its energy are printed, if `verbosity=2` additional information is printed for
all occupancies that were diagonalized. By default, `verbosity=0`.
We need also to introduce some changes in the DMFT loop with respect to the ones used for CT-QMC calculations in :ref:`advanced`.
We need also to introduce some changes in the DMFT loop with respect that used for CT-QMC calculations in :ref:`advanced`.
The hybridization function is neglected in the Hubbard-I approximation, and only non-interacting level
positions (:math:`\hat{\epsilon}=-\mu+\langle H^{ff} \rangle - \Sigma_{DC}`) are required.
Hence, instead of computing `S.G0` as in :ref:`advanced` we set the level positions ::
Hence, instead of computing `S.G0` as in :ref:`advanced` we set the level positions::
# set atomic levels:
eal = SK.eff_atomic_levels()[0]
@ -123,28 +134,35 @@ Finally, we compute the modified charge density and save it as well as correlati
Running LDA+DMFT calculations
-----------------------------
After having prepaired the script one may run one-shot DMFT calculations by executing :ref:`Ce-gamma-script` with :program:`pytriqs` in one-processor ::
After having prepared the script one may run one-shot DMFT calculations by
executing :ref:`Ce-gamma-script` with :program:`pytriqs` on a single processor::
pytriqs Ce-gamma.py
or parallel mode ::
or in parallel mode::
mpirun pytriqs Ce-gamma.py
where :program:`mpirun` launches these calculations in parallel mode and enables MPI. The exact form of this command will, of course, depend on mpi-launcher installed in your system.
where :program:`mpirun` launches these calculations in parallel mode and
enables MPI. The exact form of this command will, of course, depend on
mpi-launcher installed in your system.
Instead of doing one-shot run one may also perform fully self-consistent LDA+DMFT calculations, as we will do in this tutorial. We launch these calculations as follows ::
Instead of doing one-shot run one may also perform fully self-consistent
LDA+DMFT calculations, as we will do in this tutorial. We launch these
calculations as follows ::
run_triqs -qdmft
where `-qdmft` flag turns on LDA+DMFT calculations with :program:`Wien2k`. We use here the default convergence criterion in :program:`Wien2k` (convergence to 0.1 mRy in energy).
where `-qdmft` flag turns on LDA+DMFT calculations with :program:`Wien2k`. We
use here the default convergence criterion in :program:`Wien2k` (convergence to
0.1 mRy in energy).
After calculations are done we may check the value of correlational ('Hubbard') energy correction to the total energy::
After calculations are done we may check the value of correlation ('Hubbard') energy correction to the total energy::
>grep HUBBARD Ce-gamma.scf|tail -n 1
HUBBARD ENERGY(included in SUM OF EIGENVALUES): -0.220502
and the band("kinetic") energy with DMFT correction::
and the band ("kinetic") energy with DMFT correction::
>grep DMFT Ce-gamma.scf |tail -n 1
KINETIC ENERGY with DMFT correction: -5.329087
@ -162,8 +180,11 @@ as well as the convergence in total energy::
Calculating DOS with Hubbard-I
------------------------------
Within Hubbard-I one may also easily obtain the spectral function ("band structure") and integrated spectral function ("density of states, DOS").
In difference with the CT-QMC approach one does not need to provide the real-frequency self-energy (see :ref:`analysis`) it can be calculated directly by the Hubbard-I solver.
Within Hubbard-I one may also easily obtain the angle-resolved spectral function (band
structure) and integrated spectral function (density of states or DOS). In
difference with the CT-QMC approach one does not need to provide the
real-frequency self-energy (see :ref:`analysis`) as it can be calculated directly
in the Hubbard-I solver.
The corresponding script :ref:`Ce-gamma_DOS-script` contains several new parameters ::

12
doc/Ce-gamma.py
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@ -8,15 +8,15 @@ U_int = 6.00
J_hund = 0.70
Loops = 2 # Number of DMFT sc-loops
Mix = 0.7 # Mixing factor in QMC
# 1.0 ... all from imp; 0.0 ... all from Gloc
DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein
DC_Mix = 1.0 # 1.0 ... all from imp; 0.0 ... all from Gloc
useBlocs = False # use bloc structure from LDA input
useMatrix = True # use the U matrix calculated from Slater coefficients instead of (U+2J, U, U-J)
Natomic = 1
HDFfilename = lda_filename+'.h5'
use_val= U_int * (Natomic - 0.5) - J_hund * (Natomic*0.5 - 0.5)
use_val= U_int * (Natomic - 0.5) - J_hund * (Natomic * 0.5 - 0.5)
# Convert DMFT input:
# Can be commented after the first run
@ -55,7 +55,7 @@ if (previous_present):
mpi.report("Using stored data for initialisation")
if (mpi.is_master_node()):
ar = HDFArchive(HDFfilename,'a')
S.Sigma <<= ar['SigmaF']
S.Sigma <<= ar['SigmaImFreq']
del ar
S.Sigma = mpi.bcast(S.Sigma)
SK.load()
@ -103,8 +103,8 @@ for Iteration_Number in range(1,Loops+1):
if ((itn>1)or(previous_present)):
if (mpi.is_master_node()and (Mix<1.0)):
mpi.report("Mixing Sigma and G with factor %s"%Mix)
if ('SigmaF' in ar):
S.Sigma <<= Mix * S.Sigma + (1.0-Mix) * ar['SigmaF']
if ('SigmaImFreq' in ar):
S.Sigma <<= Mix * S.Sigma + (1.0-Mix) * ar['SigmaImFreq']
if ('GF' in ar):
S.G <<= Mix * S.G + (1.0-Mix) * ar['GF']
@ -114,7 +114,7 @@ for Iteration_Number in range(1,Loops+1):
if (mpi.is_master_node()):
ar['SigmaF'] = S.Sigma
ar['SigmaImFreq'] = S.Sigma
ar['GF'] = S.G
# after the Solver has finished, set new double counting:

43
doc/LDADMFTmain.rst
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@ -22,16 +22,16 @@ to get the local quantities used in DMFT. It is initialized by::
The only necessary parameter is the filename of the hdf5 archive. In addition, there are some optional parameters:
* `mu`: The chemical potential at initialization. This value is only used, if there is no other value found in the hdf5 arxive. Standard is 0.0
* `h_field`: External magnetic field, standard is 0.0
* `mu`: The chemical potential at initialization. This value is only used if no other value is found in the hdf5 arxive. The default value is 0.0.
* `h_field`: External magnetic field. The default value is 0.0.
* `use_lda_blocks`: If true, the structure of the density matrix is analysed at initialisation, and non-zero matrix elements
are identified. The DMFT calculation is then restricted to these matrix elements, yielding a more efficient solution of the
local interaction problem. Also degeneracies in orbital and spin space are recognised, and stored for later use. Standard value is `False`.
local interaction problem. Degeneracies in orbital and spin space are also identified and stored for later use. The default value is `False`.
* `lda_data`, `symm_corr_data`, `par_proj_data`, `symm_par_data`, `bands_data`: These string variables define the subgroups in the hdf5 arxive,
where the corresponding information is stored. The standard values are consistent with the standard values in :ref:`interfacetowien`.
where the corresponding information is stored. The default values are consistent with those in :ref:`interfacetowien`.
At initialisation, the necessary data is read from the hdf5 file. If we restart a calculation from a previous one, also the information on
the degenerate shells, the block structure of the density matrix, the chemical potential, and double counting correction are read.
At initialisation, the necessary data is read from the hdf5 file. If a calculation is restarted based on a previous hdf5 file, information on
degenerate shells, the block structure of the density matrix, the chemical potential, and double counting correction is also read in.
.. index:: Multiband solver
@ -44,37 +44,36 @@ There is a module that helps setting up the multiband CTQMC solver. It is loaded
S = SolverMultiBand(beta, n_orb, gf_struct = SK.gf_struct_solver[0], map=SK.map[0])
The necessary parameters are the inverse temperature `beta`, the Coulomb interaction `U_interact`, the Hund's rule coupling `J_hund`,
and the number of orbitals `n_orb`. There are again several optional parameters that allow to modify the local Hamiltonian to
and the number of orbitals `n_orb`. There are again several optional parameters that allow the tailoring of the local Hamiltonian to
specific needs. They are:
* `gf_struct`: Contains the block structure of the local density matrix. Has to be given in the format as calculated by :class:`SumkLDA`.
* `map`: If `gf_struct` is given as parameter, also `map` has to be given. This is the mapping from the block structure to a general
* `gf_struct`: The block structure of the local density matrix given in the format calculated by :class:`SumkLDA`.
* `map`: If `gf_struct` is given as parameter, `map` also must be given. This is the mapping from the block structure to a general
up/down structure.
The solver method is called later by this statement::
S.solve(U_interact,J_hund,use_spinflip=False,use_matrix=True,
l=2,T=None, dim_reps=None, irep=None, deg_orbs=[],n_cycles =10000,
l=2,T=None, dim_reps=None, irep=None, n_cycles =10000,
length_cycle=200,n_warmup_cycles=1000)
The parameters for the Coulomb interaction `U_interact` and the Hunds coupling `J_hund` are necessary parameters. The rest are optional parameters, for which default values are set.
They denerally should be reset for a given problem. Their meaning is as follows:
The parameters for the Coulomb interaction `U_interact` and the Hund's coupling `J_hund` are necessary input parameters. The rest are optional
parameters for which default values are set. Generally, they should be reset for the problem at hand. Here is a description of the parameters:
* `use_matrix`: If `True`, the interaction matrix is calculated from Slater integrals, which are calculated from `U_interact` and
* `use_matrix`: If `True`, the interaction matrix is calculated from Slater integrals, which are computed from `U_interact` and
`J_hund`. Otherwise, a Kanamori representation is used. Attention: We define the intraorbital interaction as
`U_interact`, the interorbital interaction for opposite spins as `U_interact-2*J_hund`, and interorbital for equal spins as
`U_interact-3*J_hund`!
* `T`: A matrix that transforms the interaction matrix from spherical harmonics, to a symmetry adapted basis. Only effective, if
`use_matrix=True`.
* `l`: Orbital quantum number. Again, only effective for Slater parametrisation.
* `deg_orbs`: A list that gives the degeneracies of the orbitals. It is used to set up a global move of the CTQMC solver.
`U_interact-3*J_hund`.
* `T`: The matrix that transforms the interaction matrix from spherical harmonics to a symmetry-adapted basis. Only effective for Slater
parametrisation, i.e. `use_matrix=True`.
* `l`: The orbital quantum number. Again, only effective for Slater parametrisation, i.e. `use_matrix=True`.
* `use_spinflip`: If `True`, the full rotationally-invariant interaction is used. Otherwise, only density-density terms are
kept in the local Hamiltonian.
* `dim_reps`: If only a subset of the full d-shell is used a correlated orbtials, one can specify here the dimensions of all the subspaces
* `dim_reps`: If only a subset of the full d-shell is used as correlated orbtials, one can specify here the dimensions of all the subspaces
of the d-shell, i.e. t2g and eg. Only effective for Slater parametrisation.
* `irep`: The index in the list `dim_reps` of the subset that is used. Only effective for Slater parametrisation.
* `n_cycles`: Number of CTQMC cycles (a sequence of moves followed by a measurement) per core. The default value of 10000 is the minimum, and generally should be incresed
* `length_cycle`: Number of CTQMC moves per one cycle
* `n_cycles`: Number of CTQMC cycles (a sequence of moves followed by a measurement) per core. The default value of 10000 is the minimum, and generally should be increased.
* `length_cycle`: Number of CTQMC moves per one cycle.
* `n_warmup_cycles`: Number of initial CTQMC cycles before measurements start. Usually of order of 10000, sometimes needs to be increased significantly.
Most of above parameters can be taken directly from the :class:`SumkLDA` class, without defining them by hand. We will see a specific example
@ -98,7 +97,7 @@ set up the loop over DMFT iterations and the self-consistency condition::
S.G0 <<= inverse(S.Sigma + inverse(S.G)) # finally get G0, the input for the Solver
S.solve(U_interact,J_hund,use_spinflip=False,use_matrix=True, # now solve the impurity problem
l=2,T=None, dim_reps=None, irep=None, deg_orbs=[],n_cycles =10000,
l=2,T=None, dim_reps=None, irep=None, n_cycles =10000,
length_cycle=200,n_warmup_cycles=1000)
dm = S.G.density() # density matrix of the impurity problem

6
doc/advanced.rst
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@ -76,7 +76,7 @@ of the last iteration::
if (previous_present):
if (mpi.is_master_node()):
ar = HDFArchive(lda_filename+'.h5','a')
S.Sigma <<= ar['SigmaF']
S.Sigma <<= ar['SigmaImFreq']
del ar
S.Sigma = mpi.bcast(S.Sigma)
@ -103,7 +103,7 @@ previous section, with some additional refinement::
# Solve the impurity problem:
S.solve(U_interact=U,J_hund=J,use_spinflip=use_spinflip,use_matrix=use_matrix,
l=l,T=SK.T[0], dim_reps=SK.dim_reps[0], irep=2, deg_orbs=SK.deg_shells[0],n_cycles =qmc_cycles,
l=l,T=SK.T[0], dim_reps=SK.dim_reps[0], irep=2, n_cycles=qmc_cycles,
length_cycle=length_cycle,n_warmup_cycles=warming_iterations)
# solution done, do the post-processing:
@ -112,7 +112,7 @@ previous section, with some additional refinement::
S.Sigma <<=(inverse(S.G0)-inverse(S.G))
# Solve the impurity problem:
S.solve(U_interact=U,J_hund=J,use_spinflip=use_spinflip,use_matrix=use_matrix,
l=l,T=SK.T[0], dim_reps=SK.dim_reps[0], irep=2, deg_orbs=SK.deg_shells[0],n_cycles =qmc_cycles,
l=l,T=SK.T[0], dim_reps=SK.dim_reps[0], irep=2, n_cycles=qmc_cycles,
length_cycle=length_cycle,n_warmup_cycles=warming_iterations)
# solution done, do the post-processing:

104
doc/analysis.rst
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@ -1,39 +1,37 @@
.. _analysis:
Analysing tools
===============
Tools for analysis
==================
This section explains how to use some tools of the package in order to analyse the data.
.. warning::
The package does NOT provide an explicit method to do an analytic continuation of the
The package does NOT provide an explicit method to do an **analytic continuation** of the
self energies and Green functions from Matsubara frequencies to the real frequancy axis!
There are methods included e.g. in the ALPS package, which can be used for these purposes. But
be careful: All these methods have to be used very carefully!!
There are methods included e.g. in the :program:`ALPS` package, which can be used for these purposes. But
be careful: All these methods have to be used very carefully!
The analysing tools can be found in an extension of the :class:`SumkLDA` class, they are
loaded by::
The tools for analysis can be found in an extension of the :class:`SumkLDA` class and are
loaded by importing the module :class:`SumkLDATools`::
from pytriqs.applications.dft.sumk_lda_tools import *
This import the module ``SumkLDATools``. There are two practical tools, for which you don't
need a self energy on the real axis:
There are two practical tools for which you do not need a self energy on the real axis, namely the:
* The density of states of the Wannier orbitals.
* Partial charges according to the Wien2k definition.
* density of states of the Wannier orbitals,
* partial charges according to the Wien2k definition.
Other routines need the self energy on the real frequency axis. If you managed to get them, you can
calculate
The self energy on the real frequency axis is necessary in computing the:
* the momentum-integrated spectral function including self-energy effects.
* the momentum-resolved spectral function (i.e. ARPES)
* momentum-integrated spectral function including self-energy effects,
* momentum-resolved spectral function (i.e. ARPES).
The initialisation of the class is completely equivalent to the initialisation of the :class:`SumkLDA`
The initialisation of the class is equivalent to that of the :class:`SumkLDA`
class::
SK = SumkLDATools(hdf_file = filename)
By the way, all routines available in :class:`SumkLDA` are also available here.
Note that all routines available in :class:`SumkLDA` are also available here.
Routines without real-frequency self energy
-------------------------------------------
@ -43,22 +41,22 @@ density of states of the Wannier orbitals, you simply type::
SK.check_input_dos(om_min, om_max, n_om)
which produces plots between real frequencies `om_min` and `om_max`, using a mesh of `n_om` points. There
is an optional parameter, `broadening`, which defines an additional Lorentzian broadening, and is set to `0.01`
by default.
which produces plots between the real frequencies `om_min` and `om_max`, using a mesh of `n_om` points. There
is an optional parameter `broadening` which defines an additional Lorentzian broadening, and has the default value of
`0.01` by default.
Since we can calculate the partial charges directly from the Matsubara Green's functions, we also don't need a
real frequency self energy for this purpose. The calculation is done by::
Since we can calculate the partial charges directly from the Matsubara Green's functions, we also do not need a
real-frequency self energy for this purpose. The calculation is done by::
ar = HDFArchive(SK.hdf_file)
SK.put_Sigma([ ar['SigmaF'] ])
SK.put_Sigma([ ar['SigmaImFreq'] ])
del ar
dm = SK.partial_charges()
which calculates the partial charges using the input that is stored in the hdf5 file (self energy, double counting,
chemical potential). Here we assumed that the final self energy is stored as `SigmaF` in the archive.
On return, dm is a list, where the list items correspond to the density matrices of all shells
defined in the list ``SK.shells``. This list is constructed by the Wien2k converter routines and stored automatically
which calculates the partial charges using the data stored in the hdf5 file, namely the self energy, double counting, and
chemical potential. Here we assumed that the final self energy is stored as `SigmaImFreq` in the archive.
On return, `dm` is a list, where the list items correspond to the density matrices of all shells
defined in the list `SK.shells`. This list is constructed by the Wien2k converter routines and stored automatically
in the hdf5 archive. For the detailed structure of `dm`, see the reference manual.
@ -69,11 +67,10 @@ In order to plot data including correlation effects on the real axis, one has to
Most conveniently, it is stored as a real frequency :class:`BlockGf` object in the hdf5 file::
ar = HDFArchive(filename+'.h5','a')
ar['SigmaReFreq'] = Sigma_real
ar['SigmaReFreq'] = SigmaReFreq
del ar
You may also store it in text files. If all blocks of your self energy are of dimension 1x1 you store them in `fname_(block)0.dat` files. Here `(block)` is a block name (`up`, `down`, or combined `ud`). In the case when you have matrix blocks, you store them in `(i)_(j).dat` files (where `(i)` and `(j)` are the orbital indices) in the `fname_(block)` directory
You may also store it in text files. If all blocks of your self energy are of dimension 1x1, you store them in `fname_(block)0.dat` files. Here `(block)` is a block name (`up`, `down`, or combined `ud`). In the case when you have matrix blocks, you store them in `(i)_(j).dat` files, where `(i)` and `(j)` are the orbital indices, in the `fname_(block)` directory.
This self energy is loaded and put into the :class:`SumkLDA` class by the function::
@ -81,47 +78,42 @@ This self energy is loaded and put into the :class:`SumkLDA` class by the functi
where:
* `filename` is the name of the hdf5 archive file or the `fname` pattern in text files names as described above.
* `hdf=True`: the real-axis self energy will be read from the hdf5 file, `hdf=False`: from the text files
* `hdf_dataset` the name of dataset where the self energy is stored in the hdf5 file
* `n_om` number of points in the real-axis mesh (used only if `hdf=False`)
* `filename`: the name of the hdf5 archive file or the `fname` pattern in text files names as described above,
* `hdf`: if `True`, the real-axis self energy will be read from the hdf5 file, otherwise from the text files,
* `hdf_dataset`: the name of dataset where the self energy is stored in the hdf5 file,
* `n_om`: the number of points in the real-axis mesh (used only if `hdf=False`).
The chemical potential as well as the double counting correction were already read in the initialisation process.
The chemical potential as well as the double
counting correction were already read in the initialisation process.
With this self energy, we can do now::
With this self energy, we can now execute::
SK.dos_partial(broadening=broadening)
This produces the momentum-integrated spectral functions (density of states, DOS), also orbitally resolved.
The variable `broadening` is an additional Lorentzian broadening that is added to the resulting spectra.
This produces both the momentum-integrated (total density of states or DOS) and orbitally-resolved (partial/projected DOS) spectral functions.
The variable `broadening` is an additional Lorentzian broadening applied to the resulting spectra.
The output is printed into the files
* `DOScorr(sp).dat`: The total DOS. `(sp)` stands for `up`, `down`, or combined `ud`. The latter case
* `DOScorr(sp).dat`: The total DOS, where `(sp)` stands for `up`, `down`, or combined `ud`. The latter case
is relevant for calculations including spin-orbit interaction.
* `DOScorr(sp)_proj(i).dat`: The DOS projected to an orbital with index `(i)`. The index `(i)` refers to
the indices given in ``SK.shells``.
* `DOScorr(sp)_proj(i)_(m)_(n).dat`: Sames as above, but printed as orbitally resolved matrix in indices
`(m)` and `(n)`. For `d` orbitals, it gives separately the DOS
for, e.g., :math:`d_{xy}`, :math:`d_{x^2-y^2}`, and so on.
* `DOScorr(sp)_proj(i)_(m)_(n).dat`: As above, but printed as orbitally-resolved matrix in indices
`(m)` and `(n)`. For `d` orbitals, it gives the DOS seperately for, e.g., :math:`d_{xy}`, :math:`d_{x^2-y^2}`, and so on.
Another quantity of interest is the momentum-resolved spectral function, which can directly be compared to ARPES
experiments. We assume here that we already converted the output of the :program:`dmftproj` program with the
converter routines, see :ref:`interfacetowien`. The spectral function is calculated by::
converter routines (see :ref:`interfacetowien`). The spectral function is calculated by::
SK.spaghettis(broadening)
The output is
written as the 3-column files ``Akw(sp).dat``, where `(sp)` has the same meaning as above. The output format is
`k`, :math:`\omega`, `value`. Optional parameters are
Optional parameters are
* `shift`: An additional shift, added as `(ik-1)*shift`, where `ik` is the index of the `k` point. Useful for plotting purposes,
standard value is 0.0.
* `plotrange`: A python list with two entries, first being :math:`\omega_{min}`, the second :math:`\omega_{max}`, setting the plot
range for the output. Standard value is `None`, in this case the momentum range as given in the self energy is plotted.
* `ishell`: If this is not `None` (standard value), but an integer, the spectral function projected to the orbital with index `ishell`
is plotted to the files. Attention: The spectra are not rotated to the local coordinate system as used in the :program:`Wien2k`
program (For experts).
* `shift`: An additional shift added as `(ik-1)*shift`, where `ik` is the index of the `k` point. This is useful for plotting purposes.
The default value is 0.0.
* `plotrange`: A list with two entries, :math:`\omega_{min}` and :math:`\omega_{max}`, which set the plot
range for the output. The default value is `None`, in which case the full momentum range as given in the self energy is used.
* `ishell`: An integer denoting the orbital index `ishell` onto which the spectral function is projected. The resulting function is saved in
the files. The default value is `None`. Note for experts: The spectra are not rotated to the local coordinate system used in :program:`Wien2k`.
The output is written as the 3-column files ``Akw(sp).dat``, where `(sp)` is defined as above. The output format is
`k`, :math:`\omega`, `value`.

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doc/install.rst
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@ -57,7 +57,9 @@ Installation steps
In addition, :file:`path_to_Wien2k/SRC_templates` also contains
:program:`run_triqs` and :program:`runsp_triqs` scripts for running Wien2k+DMFT
fully self-consistent calculations. These files should be copied to
:file:`path_to_Wien2k`.
:file:`path_to_Wien2k`, and set as executables by running::
$ chmod +x run*_triqs
You will also need to insert manually a correct call of :file:`pytriqs` into
these scripts using an appropriate for your system MPI wrapper (mpirun,

23
doc/interface.rst
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@ -33,14 +33,14 @@ an hdf5 arxive, named :file:`material_of_interest.h5`, where all the data is sto
There are three optional parameters to the Constructor:
* `lda_subgrp`: We store all data in sub groups of the hdf5 arxive. For the main data
that is needed for the DMFT loop, we use the sub group specified by this optional parameter.
If it is not given, the standard value `SumK_LDA` is used as sub group name.
* `symm_subgrp`: In this sub group we store all the data for applying the symmetry
operations in the DMFT loop. Standard value is `SymmCorr`.
* `lda_subgrp`: We store all data in subgroups of the hdf5 arxive. For the main data
that is needed for the DMFT loop, we use the subgroup specified by this optional parameter.
The default value `SumK_LDA` is used as the subgroup name.
* `symm_subgrp`: In this subgroup we store all the data for applying the symmetry
operations in the DMFT loop. The default value is `SymmCorr`.
* `repacking`: If true, and the hdf5 file already exists, the system command :program:`h5repack`
is invoked. This command ensures a minimal file size of the hdf5
file. Standard value is `False`. If you want to use this, be sure
file. The default value is `False`. If you wish to use this, ensure
that :program:`h5repack` is in your path variable!
After initialising the interface module, we can now convert the input text files into the
@ -48,7 +48,7 @@ hdf5 arxive by::
Converter.convert_dmft_input()
This reads all the data, and stores it in the sub group `lda_subgrp`, as discussed above.
This reads all the data, and stores it in the subgroup `lda_subgrp`, as discussed above.
In this step, the files :file:`material_of_interest.ctqmcout` and :file:`material_of_interest.symqmc`
have to be present in the working directory.
@ -70,17 +70,16 @@ of :program:`Wien2k`, you have to use::
This reads the files :file:`material_of_interest.parproj` and :file:`material_of_interest.sympar`.
Again, there are two optional parameters
* `par_proj_subgrp`: The sub group, where the data for the partial projectors is stored. Standard
is `SumK_LDA_ParProj`.
* `symm_par_subgrp`: Sub group for the symmetry operations, standard value is `SymmPar`.
* `par_proj_subgrp`: The subgroup for partial projectors data. The default value is `SumK_LDA_ParProj`.
* `symm_par_subgrp`: The subgroup for symmetry operations data. The default value is `SymmPar`.
Another routine of the class allows to read the input for plotting the momentum-resolved
spectral function. It is done by::
Converter.convert_bands_input()
The optional parameter, which tells the routine where to store the data is here `bands_subgrp`,
and its standard value is `SumK_LDA_Bands`.
The optional parameter that controls where the data is stored is `bands_subgrp`,
with the default value `SumK_LDA_Bands`.
After having converted this input, you can further proceed with the :ref:`analysis`.

34
doc/selfcons.rst
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@ -15,14 +15,13 @@ codes is also possible.
We can use the DMFT script as introduced in sections :ref:`LDADMFTmain` 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. Then we set the variable
`lda_filename` dynamically::
:ref:`interfacetowien` for details. We can then set the variable `lda_filename` dynamically::
import os
lda_filename = os.getcwd().rpartition('/')[2]
This sets the `lda_filename` to the name of the current directory. The reminder of the scripts is completely the
same as in one-shot calculations. Only at the very end we have to calculate the modified charge density,
This sets the `lda_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::
@ -30,28 +29,28 @@ previous section, we symmetrise the self energy, and recalculate the impurity Gr
S.G <<= inverse(S.G0) - S.Sigma
S.G.invert()
These steps are not necessary, but can help to reduce fluctuation of the total energy.
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 ])
chemical_potential = SK.find_mu( precision = 0.000001 )
dN,d = SK.calc_density_correction(filename = lda_filename+'.qdmft')
dN, d = SK.calc_density_correction(filename = lda_filename+'.qdmft')
SK.save()
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 `Filename`, which is later read by the :program:`Wien2k` program. The last statement saves
is stored in the file `lda_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 * S.Sigma).total_density()
From this value, we have to substract the double counting energy::
From this value, we substract the double counting energy::
correnerg -= SK.dc_energ[0]
and save this value into the file::
and save this value too::
if (mpi.is_master_node()):
f=open(lda_filename+'.qdmft','a')
@ -61,23 +60,23 @@ and save this value into the file::
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.
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, you start your calculation for instance by::
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 read from the `case.qdmft`
file, and 10 self-consitency iterations are done. If you run the code on a parallel machine, you can specify the number of
nodes that are used::
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
with the `-np` flag. 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
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
@ -85,5 +84,4 @@ unstable convergence, you have to adjust the parameters such as
`loops`, `mix`, or `Delta_mix` to improve the convergence.
In the next section, :ref:`LDADMFTtutorial`, we will see in a detailed
example, how such a self consistent calculation is performed.
example how such a self consistent calculation is performed.