Tutorial 1: MnO
Tutorial 1.a. : Preparing the input files
We begin by a simple example of a charge transfer insulator: MnO. It
cristalizes in the rock salt structure (two interpenetrating cubic FCC
str.) with lattice parameter
8.401155a a.u.. The Mn ion contains around 5 electrons, which
form the high spin state. The ground state has a large gap, which is
usually categorized as the charge transfer gap.
We will treat dynamically the Mnd electrons.
The first step is to perform a DFT calculation to get a good
charge density. (We assume that the user has some familiarity with the
Wien2K code and we give only a very brief introductions to the DFT
part.)
First create a directory with the name MnO and copy the MnO.struct file into it. [Note that Wien2k requires to call directory
with the same name as the structure file].
The structure file contains the details of the crystal structure in the format
that Wien2K uses. Initialize the DFT calculation by using
init_lapw
and then choosing the default options. In version 14, one needs to
type the following sequence of commands:
Enter reduction in %: 0
Use old or new scheme (o/N) : N
Do you want to accept these radii; .... (a/d/r) : a
specify nnbondlength factor: 2
CtrlXC
continue with sgroup : c
CtrlXC
continue with symmetry : c
CtrlXC
continue with lstart : c
specify switches for instgen_lapw (or press ENTER): ENTER
SELECT XCPOT: 13
SELECT ENERGY : 6
CtrlXC
continue with kgen : c
CtrlXC
CtrlXC
NUMBER OF KPOINTS : 500
CtrlXC
continue with dstart or execute kgen again or exit (c/e/x) : c
CtrlXC
do you want to perform a spinpolarized calculation : n
Here CtrlXC stands for breaking out of your editor. CtrlXC is used
in emacs and similar editors, but in vi, one should
replace CtrlXC by :q!.
These are pretty much the default values that
Wien2K uses. We chose to work with the PBE functional (note that
total energies are better with LDA+DMFT functional, while spectra is
almost identical when using PBE or LDA), and use a
kpoint grid of 500 points (in the whole Brillouin zone). Note
that spinorbit interaction can be safely ignored as there are no
heavy ions in the system.
Note that instead of using the interactive way of determining
the DFT parameters, one can run wien2k initialization in batch mode,
by executing init_lapw with parameters:
init_lapw b vxc 13 ecut 6.0 rkmax 7 numk 500
Next, we can run the DFT calculation using the
command
run_lapw
In DMFT calculation, one needs converged
frequency dependent local Green's function, hence more kpoints are
typically needed. We will increase the number of kpoints to 2000
in this tutorial. To do that, run
x kgen
and specify 2000 kpoints. Next, rerun wien2k on this kmesh,
i.e., run_lapw NI
(This step is not essential as the DFT charge
will not be very precise anyway for our DFT+EmbeddedDMFT calculation).
Once the DFT calculation is done, we have a good charge
density to start a DMFT calculation. The next step is to initialize
the DMFT calculation. For this, we use the script
init_dmft.py.
Note that we can execute this python script interactively, or, with
giving parameters to the executable (batch mode).
Using faster batch mode, we might want to initialize dmft by
init_dmft.py ca 1 ot d qs 7
The switch ca specifies the correlated atom, ot
specifies the orbital type, and qs specifies the cubic
symmetry that we are going to use for this orbitals. These options
are explained below.
Alternatively, we might want to use the script
init_dmft.py in interactive mode, in which case we
just execute it, and answer the following questions:
Specify correlated atoms (ex: 14,7,8): 1
Do you want to continue; or edit again? (c/e): c
For each atom, specify correlated orbital(s) (ex: d,f):
1 Mn: d
Do you want to continue; or edit again? (c/e): c
Specify qsplit for each correlated orbital (default = 0):
1 Mn1 d: 7
Do you want to continue; or edit again? (c/e): c
Specify projector type (default = 5): 5
Do you want to continue; or edit again? (c/e): c
Do you want to group any of these orbitals into clusterDMFT problems? (y/n): n
Enter the correlated problems forming each unique correlated
problem, separated by spaces (ex: 1,3 2,4 58): 1
Do you want to continue; or edit again? (c/e): c
Range (in eV) of hybridization taken into account in impurity
problems; default 10.0, 10.0: <ENTER>
Perform calculation on real; or imaginary axis? (i/r) (default=i) : i
CtrlXC
Is this a spinorbit run? (y/n): n
CtrlXC
Now we want to explain the options and their choice:

At the beginning, the code shows all atoms in the unit cell:
There are 2 atoms in the unit cell:
1 Mn
2 O
Specify correlated atoms (ex: 14,7,8): 1
and asks which one should be treated dynamically. We choose only the
first atom, Mn, to be treated as a correlated atom in the DMFT step.
 The code next asks which orbital quantum numbers should be
treated dynamically:
For each atom, specify correlated orbital(s) (ex: d,f):
1 Mn: d
The partially filled d shell of the Mn ion is to be treated in DMFT.

Next we should specify what type of local symmetry we have on the
given atom, and if we possibly want to correlated only a subshell
Specify qsplit for each correlated orbital (default = 0):
Qsplit Description
 
0 average GF, noncorrelated
1 j,mj> basis, no symmetry, except time reversal (jz=jz)
1 j,mj> basis, no symmetry, not even time reversal (jz=jz)
2 real harmonics basis, no symmetry, except spin (up=dn)
2 real harmonics basis, no symmetry, not even spin (up=dn)
3 t2g orbitals
3 eg orbitals
4 j,mj>, only l1/2 and l+1/2
5 axial symmetry in real harmonics
6 hexagonal symmetry in real harmonics
7 cubic symmetry in real harmonics
8 axial symmetry in real harmonics, up different than down
9 hexagonal symmetry in real harmonics, up different than down
10 cubic symmetry in real harmonics, up different then down
11 j,mj> basis, nonzero off diagonal elements
12 real harmonics, nonzero off diagonal elements
13 J_eff=1/2 basis for 5d ions, nonmagnetic with symmetry
14 J_eff=1/2 basis for 5d ions, no symmetry
 
1 Mn1 d: 7
This chooses the local basis for the DMFT calculation. Notice that
current implementation of the DMFT in combination with CTQMC is basis
dependent. This is because by default we ignore offdiagonal
components of the hybridization function. (The code also supports
offdiagonal hybridization, but its use requires more advanced
options, and leads to a minus sign problem in the solution of the impurity.)
In MnO, we ignored the spinorbit coupling, and
the crystal symmetry is cubic, hence hybridization is exactly
diagonal in the cubic harmonics basis. We thus want to use
this basis, which is the option 7. We could also use option 2, but
this would lead to a slight worse statistics as impurity solver now
averages over 3 t2g (or 2 eg) equivalent orbitals.

Specify projector type (default = 5):
Projector Drscription
 
1 projection to the solution of Dirac equation (to the head)
2 projection to the Dirac solution, its energy derivative,
LO orbital, as described by P2 in PRB 81, 195107 (2010)
4 similar to projector2, but takes fixed number of bands in
some energy range, even when chemical potential and
MTzero moves (folows band with certain index)
5 fixed projector, which is written to projectorw.dat. You can
generate projectorw.dat with the tool wavef.py
 
> 5
As discussed above, DMFT requires a local basis, i.e., projector to
local Green's function. If we could treat by DMFT all degrees of
freedom on a given atom, the solution would not depend on the choice
of the projector (it would depend on the range though). Fortunately,
the orbitals on Mn ion are quite nicely separated in energy, and the only
narrow states close to EF on Mn ion are the 3d states, hence they become
correlated. Note that the Mn 4s orbital is not far above EF, but the
4s state is very spatially extended, hence weakly correlated.
All choices of the projector project to spherical/cubic harmonics
inside a sphere around an atom, but the radial dependence of the
function is slightly different. "Projector 1" chooses solution of the
Dirac equation for the radial part, "Projector 2" chooses a
combination of the Dirac equation solution and its energy derivative,
"Projector 4" has the same radial component as Projector 2, but when
choosing an energy range for the projector, it will use equal number
of bands at each kpoint (projector will follow bands). Finally,
projector 5 fixes the projector to the solution of the Dirac equation
on the LDA level.
If we are interested in the total free energy of the system, we should
use projector 5, because in this case the projector is fixed during
DMFT selfconsistent run, which guarantees that the DFT+EmbeddedDMFT method is
stationary.
 Cluster versus singlesite DMFT option:
Do you want to group any of these orbitals into clusterDMFT problems? (y/n): n
We do not attempt to do a clusterDMFT calculation at this stage.
 For efficiency reasons, we want to solve impurity only for those
atoms which are different (and not related by symmetry). While we
could figure this out from the structure file, we allow the user to
set this here independently of the symmetry in the structure
file. This allows one to treat spontaneously broken states (such as
magnetically ordered states) in DMFT without reducing the crystal
symmetry.
Enter the correlated problems forming each unique correlated
problem, separated by spaces (ex: 1,3 2,4 58): 1
There is only one impurity in this system anyway.
 Choice for the energy range in the KohnSham basis
Range (in eV) of hybridization taken into account in impurity
problems; default 10.0, 10.0:
This sets the energy range around the Fermi level, where the hybridizations
will be taken into account. Usually a value of the order of U is a good
starting guess, but one should check the resultant hybridization
to see that it is enough. "Projector 1" and "Projector 2" will
use all bands in this energy range. "Projector 4" and "5" will use the
same set of bands at each kpoint, and to faithfully represent
the states in this energy range, it will automatically increase
the energy range in some kpoints.

Perform calculation on real; or imaginary axis? (r/i): i
The CTQMC is an imaginary axis method.

Is this a spinorbit run? (y/n): n
The spinorbit coupling is ignored.
The init_dmft.py script generates two mandatory input files,
case.indmfl and case.indmfi, and also
projectorw.dat when fixed projector=5 is used. These
files connect the solid and the impurity with the DMFT engine, respectively.
Let us take a closer look at the header of the case.indmfl file:
5 15 1 5 # hybridization band index nemin and nemax, renormalize for interstitials, projection type
1 0.025 0.025 200 3.000000 1.000000 # matsubara, broadeningcorr, broadeningnoncorr, nomega, omega_min, omega_max (in eV)
1 # number of correlated atoms
1 1 0 # iatom, nL, locrot
2 7 1 # L, qsplit, cix
The first line specifies the hybridization window. We set it to
"(10eV,10eV)" around EF, which it turns out, contains bands numbered
from 515. For each kpoint we will use the same bands, i.e., we will
follow the bands with the projector.
The same line also specified the projector type, which is 5.
The second line contains a switch for the realaxis or imaginary axis
calculation. If we require the local green's function or density of
states on real axis, we will set the switch "matsubara" to 0. Here, we
will work on imaginary axis, hence "matsubara" is set to 1.
The second line also contains information on small lorentzian broadening in
kpoint summation. The last three numbers are used for plotting the
spectral functions, to which we will return latter.
The third line specifies the number of correlated atoms.
The forth line specifies which atom (from case.struct) is
correlated (Note that each atom in the structure file counts, even when
several atoms in structure files are equivalent). The forth line also
specifies how many orbital indeces (different L's) we will use for
projection, and calculation of local green's function and partial
dos. The last index "locrot" is used for rotation of local axis, and
is not needed here (set to 0).
The fifth line specifies orbital momentum "l=2", "qsplit=7" which we
chose during initialization. Each correlated orbitalset gets a
unique index during initialization. This correlated set was given index
"cix=1". For each correlated set, a more detailed specification is needed,
which is given in the remaining of "case.indmfl" file.
The specification of correlated set is given in the second part of case.indmfl:
#================ # Siginds and crystalfield transformations for correlated orbitals ================
1 5 2 # Number of independent kcix blocks, max dimension, max numindependentcomponents
1 5 2 # cixnum, dimension, numindependentcomponents
# # Independent components are 
'eg' 't2g'
# # Sigind follows 
1 0 0 0 0
0 1 0 0 0
0 0 2 0 0
0 0 0 2 0
0 0 0 0 2
# # Transformation matrix follows 
0.00000000 0.00000000 0.00000000 0.00000000 1.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
0.70710679 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.70710679 0.00000000
0.00000000 0.00000000 0.70710679 0.00000000 0.00000000 0.00000000 0.70710679 0.00000000 0.00000000 0.00000000
0.00000000 0.00000000 0.00000000 0.70710679 0.00000000 0.00000000 0.00000000 0.70710679 0.00000000 0.00000000
0.70710679 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.70710679 0.00000000
The first line specifies the number of all correlated blocks,
the dimension of the largest correlated block, and number of orbital
components which differ.
The second line specifies the dimension of the first correlated block
(here "d" has dimension 5), and the two eg and the three t2g components are treated
as degenerate.
The transformation matrix from spheric harmonics to the DMFT basis
is given in the end, and shows how these orbitals are defined:
It is the expansion of each of these orbitals in spherical
harmonics. The columns correspond to the following quantum numbers
(m_l=2,1,0,1,2) and rows to ('z^2','x^2y^2','xz','yz','xy'). For
example, the 'z^2' orbital, which is expanded in the first line, is
equal to the m=0 spherical harmonic, whereas the yz orbital on the
forth line is an equal superposition of m=1 and m=1, multiplied by i
(note that two consecutive real numbers define one complex
number).
Note that the transformation matrix must be unitary.
This file case.indmfl can be edited in text editor and can be adjusted if necessary.
The second file, created at the initialization, is case.indmfi and
contains the following lines:
1 # number of sigind blocks
5 # dimension of this sigind block
1 0 0 0 0
0 1 0 0 0
0 0 2 0 0
0 0 0 2 0
0 0 0 0 2
In this simplest case, where we have a single impurity problem, no new
information is contained in case.indmfi file, just the "Sigind" block
from case.indmfl is repeated. We will show more complex use of this
file later.
Now we want to start DMFT with the minimal number of necessary files.
To do this, create a new folder (with any name), and when in that folder, use the
command
dmft_copy.py <dft_results_directory>
where <dft_results_directory> is the directory where the
output of the DFT calculation with DMFT initialization is. This
command copies the necessary files to start a DMFT calculation. These
include the MnO.struct file as well as the various Wien2K input files
such as MnO.inm, MnO.in1, etc. The charge density file, MnO.clmsum, is also
copied. This is all we need from the initial Wien2K run, and now we
proceed to create the rest of the files, needed by the DMFT
calculation.
Copy the params.dat file.
This file contains information about the impurity solver and
additional parameteres, which appear in DFT+EmbeddedDMFT. It is
written in Python format, and hence any Python syntax is accepted. For
checking the correct syntax, one can execute it as python script.
Its content is:
solver = 'CTQMC' # impurity solver
max_dmft_iterations = 1 # number of iteration of the dmftloop only
max_lda_iterations = 100 # number of iteration of the LDAloop only
finish = 10 # number of iterations of full charge loop (1 = no charge selfconsistency).
# You should probably use 30 or so, but for testing 10 is OK.
ntail = 300 # on imaginary axis, number of points in the tail of the logarithmic mesh
cc = 5e6 # the charge density precision to stop the LDA+DMFT run
ec = 5e6 # the energy precision to stop the LDA+DMFT run
recomputeEF = 0 # Recompute EF in dmft2 step. If recomputeEF = 0, EF is fixed. Good for insulators.
DCs = 'exactd' # exact DC with dielectric model
wbroad = 0.0 # broadening of sigma on the imaginary axis
kbroad = 0.0 # broadening of sigma on the imaginary axis
# Impurity problem number 0
iparams0={"exe" : ["ctqmc" , "# Name of the executable"],
"U" : [9.0 , "# Coulomb repulsion (F0)"],
"J" : [1.14 , "# Coulomb J"],
"CoulombF" : ["'Ising'" , "# Densitydensity form. Can be changed to 'Full' "],
"beta" : [38.68 , "# Inverse temperature"],
"svd_lmax" : [25 , "# We will use SVD basis to expand G, with this cutoff"],
"M" : [5e6 , "# Total number of Monte Carlo steps per core"],
"mode" : ["SH" , "# We will use Greens function sampling, and Hubbard I tail"],
"nom" : [100 , "# Number of Matsubara frequency points sampled"],
"tsample" : [30 , "# How often to record measurements"],
"GlobalFlip" : [500000 , "# How often to try a global flip"],
"warmup" : [1e5 , "# Warmup number of QMC steps"],
"nf0" : [5.0 , "# Nominal valence we expect. Used only for starting DC."],
}
Note that this input file is a python script, and the correcteness of
its syntax can be
checked with Python interpreter by tying
python params.dat.
Below we explain the meaning of the variables
There is only one more file that we need: A blank self energy (sig.inp)
file. Generate it by the command
One can add parameters to the script, like
szero.py e 38.22 T 0.0258531540847983
in which case we set starting doublecounting to 38.22 and temperature
to 0.0258531540847983eV. If we do not specify these values, "szero.py"
will read params.dat file, and extract this information from "beta"
and "nf0". Note that if one start with a very wrong doublecounting energy,
one will need very many DMFT iterations before reaching the
correct physical regime. For the initial guess, we read "nf0" from the
params file, and we use Anisimov's approximation for the doublecounting,
given by
V_{DC}=U*(nf01/2)J/2*(nf01).
Tutorial 1.b. : Submitting the job
Now we are ready to run DFT+EmbeddedDMFT calculation. In
the simplest case, we can just run the script
However, single CPU will take a really long
time and the statistics will be too bad. One typically needs at least
100 cores (but more is better  this jobs can easily scale to 100,000 cores) to achieve good statistics. [The DFT
part is most efficient when the number of cores is commensurate with
the number of kpoints]. Note also
that the parameter "M" in params file specifies the number of Monte
Carlo steps per core, therefore more cores are available, better is
the result (but it is not faster in execution).
To submit a job to a parallel computer, we need to prepare a
submit script. This script must create a file "mpi_prefix.dat", which
contains the command for mpi parallel execution.
This command however depends very much on the operating
system and environment of a supercomputer.
On many systems, this command is called "mpirun", and in this case,
the submit script would contain
echo "mpirun np $NSLOTS" > mpi_prefix.dat
where $NSLOTS stands for the number of available cores.
On some systems, the command might be:
echo "mpiexec port $port np $NSLOTS machinefile $TMPDIR/machines" > mpi_prefix.dat
We can also tune the number of OpenMP threads per mpi node, which can
be achieved by giving an environmental variable OMP_NUM_THREADS
to mpi command. In
mpich, for example, one could set
echo "mpirun np $NSLOTS env OMP_NUM_THREADS 1" > mpi_prefix.dat
and in openmpi, one would set
echo "mpirun np $NSLOTS x OMP_NUM_THREADS=1" > mpi_prefix.dat
When running the CTQMC solver, it is advisable to use one thread per
mpi core (OMP_NUM_THREADS=1). For lapw1,
dmft1 and dmft2 one should divide kpoints over available cores, and
if there are more cores available than there is kpoints, one can set
OMP_NUM_THREADS so that ncores ~ nkpt*OMP_NUM_THREADS. To distinguish
between the two different mpirun commands, we can setup
mpi_prefix.dat2 in addition to mpi_prefix.dat. The
former is used for dmft1, dmft2 while the latter is used for CTQMC.
Finally, once we created "mpi_prefix.dat" file, we should run the DFT+EmbeddedDMFT script by
$WIEN_DMFT_ROOT/run_dmft.py > nohup.dat 2>&1
Note that the python script is not run with mpi. However, the impurity
solver, and other dmft steps will be executed in parallel using
command specified in "mpi_prefix.dat".
As discussed above, if many more CPUcores are available than the number of kpoints, we can
optimize the execution further. Namely, a single kpoint can be
executed on many cores using OpenMP instructions. To use this
feature, one needs to specify "mpi_prefix.dat2" in addition to
"mpi_prefix.dat" file. The former is than used in the dft part of the
loop, while the latter is used by the impurity solver.
Clearly, "mpi_prefix.dat2" should specify the mpi command, which is started
on a subset of machines. Therefore "np" should be smaller than
"$NSLOTS" and "OMP_NUM_THREADS" should be larger than 1.
We provide a script that attempts to do that, hence you might want to
insert the following line between "....> mpi_prefix.dat" and "run_dmft.py":
$WIEN_DMFT_ROOT/createDMFprefix.py $JOBNAME.klist mpi_prefix.dat > mpi_prefix.dat2
where "$JOBNAME" should be set to wien2k case. Unfortunately mpi
execution is computer environment dependent, and therefore this script
might not run everywhere.
Before submitting the job, make sure that the computing notes have the
following environmental
variable specified: $WIENROOT, $WIEN_DMFT_ROOT, $SCRATCH.
If lapw1 and lapwso are executed through
x_dmft.py script, then these variables need to be set only on
the master node. Otherwise all compute nodes need to have these
variables set.
Also make sure that $WIEN_DMFT_ROOT is in $PYTHONPATH on the master node.
It is advisable to add $WIENROOT and $WIEN_DMFT_ROOT to the $PATH,
although it is not mandatory.
Typically, the .bashrc (or its equivalent) should contain
the following lines:
export WIENROOT=<wieninstalationdir>
export WIEN_DMFT_ROOT=<dircontainingdmftexecutables>
export SCRATCH="."
export PATH=$PATH:$WIENROOT:$WIEN_DMFT_ROOT
export PYTHONPATH=$PYTHONPATH:$WIEN_DMFT_ROOT
Note that SCRATCH does not need to point to the current directory,
instead it could be more efficient to have it set to SCRATCH=/tmp/ or
SCRATCH=/scratch/, so that the large vector files are written locally
for each process.
It might be necessary to repeat these commands (seeting variables)
also in the submit script. This of ocurse depends on the system.
Here we paste an example of the submit script for SUN Grid Engine
, but different system will require different script.
#!/bin/bash
########################################################################
# SUN Grid Engine job wrapper
########################################################################
#$ N MnO
#$ pe mpi2 216
#$ q <your_queue>
#$ j y
#$ M <email@your_institution>
#$ m e
########################################################################
source $TMPDIR/sge_init.sh
########################################################################
export WIENROOT=<your_w2k_root>
export WIEN_DMFT_ROOT=<your_dmft_root>
export PATH=$PATH:$WIENROOT:$WIEN_DMFT_ROOT
export PYTHONPATH=$PYTHONPATH:.:$WIEN_DMFT_ROOT
export SCRATCH='.'
JOBNAME="MnO" # must be equal to case name
mpi_prefix="mpiexec np $NSLOTS machinefile $TMPDIR/machines port $port env OMP_NUM_THREADS 1 envlist SCRATCH,WIEN_DMFT_ROOT,PYTHONPATH,WIENROOT"
echo $mpi_prefix > mpi_prefix.dat
$WIEN_DMFT_ROOT/createDMFprefix.py $JOBNAME.klist mpi_prefix.dat > mpi_prefix.dat2
$WIEN_DMFT_ROOT/run_dmft.py > nohup.dat 2>&1
rm f $JOBNAME.vector*
For PBE system, the script might look like
#!/bin/bash
#PBS l walltime=12:00:00,nodes=72:ppn=16
#PBS N MnO
#PBS j oe
export WIENROOT=<your_w2k_root>
export WIEN_DMFT_ROOT=<your_dmft_root>
export PYTHONPATH=$PYTHONPATH:.:$WIEN_DMFT_ROOT
export SCRATCH='.'
cd $PBS_O_WORKDIR
echo "mpirun np 1152 x OMP_NUM_THREADS=1" > mpi_prefix.dat
echo "mpirun np 72 x OMP_NUM_THREADS=16" > mpi_prefix.dat2
$WIEN_DMFT_ROOT/run_dmft.py >& nohup.dat
rm f $JOBNAME.vector*
Tutorial 1.c. : Monitoring the job
While the code is running, there are multiple files that you can check
to see how it is going. One is the ':log' file, which serves exactly
the same purpose as in Wien2K. It lists the individual
modules that are run:
Thu Mar 23 22:57:43 EDT 2017> (x) f MnO lapw0
Thu Mar 23 22:57:45 EDT 2017> lapw1
Thu Mar 23 22:57:46 EDT 2017> dmft1
Thu Mar 23 22:57:47 EDT 2017> impurity
Thu Mar 23 23:00:13 EDT 2017> dmft2
Thu Mar 23 23:00:14 EDT 2017> (x) f MnO lcore
Thu Mar 23 23:00:15 EDT 2017> (x) f MnO mixer
Thu Mar 23 23:00:16 EDT 2017> (x) f MnO lapw0
Thu Mar 23 23:00:37 EDT 2017> lapw1
Thu Mar 23 23:00:38 EDT 2017> dmft2
Thu Mar 23 23:00:41 EDT 2017> (x) f MnO lcore
Thu Mar 23 23:00:41 EDT 2017> (x) f MnO mixer
Thu Mar 23 23:00:43 EDT 2017> (x) f MnO lapw0
Thu Mar 23 23:00:45 EDT 2017> lapw1
Thu Mar 23 23:00:47 EDT 2017> dmft2
Thu Mar 23 23:00:48 EDT 2017> (x) f MnO lcore
Thu Mar 23 23:00:51 EDT 2017> (x) f MnO mixer
The code starts by running the lapw0 and lapw1 modules of Wien2K to
calculate the vector files, etc; but then, dmft1 and dmft2 steps are
inserted. Impurity is running inbetween dmft1 and dmft2
step. The dmft2 step replaces the lapw2 step of Wien2k.
Since DFT part is here very fast (much faster than impurity solver),
we used several DFT step (up to 100) in combination with a single dmft
step. Therefore after dmft1, one can see several blocks of the
following cycle repeated: dmft2,lcore,mixer,lapw0,lapw1. This is the
charge loop. The reason to do this is to obtain a well converged DFT
run between each dmft1 step, since dmft1 is the most time consuming
part (as can be seen in the timing information above). Whether the
charge density is well converged can be checked by grep'ing ':CHARGE'
in the .dayfile:
grep ':CHARGE' MnO.dayfile
Which will print
:CHARGE convergence: 0.3650051
:CHARGE convergence: 0.320649
:CHARGE convergence: 0.1980197
:CHARGE convergence: 0.1882957
:CHARGE convergence: 0.1454709
:CHARGE convergence: 0.0432447
:CHARGE convergence: 0.0190978
:CHARGE convergence: 0.0055925
:CHARGE convergence: 0.0048235
:CHARGE convergence: 0.0024782
:CHARGE convergence: 0.0006552
:CHARGE convergence: 0.0001947
:CHARGE convergence: 0.0002805
:CHARGE convergence: 2.05e05
:CHARGE convergence: 1.16e05
:CHARGE convergence: 1.69e05
:CHARGE convergence: 1.84e05
:CHARGE convergence: 1.4e06
Note that the charge converges at fixed selfenergy, but when
the selfenergy is updated (after dmft1 step) the charge is again
nonconverged.
Towards the end of the run (after
about 20 cycles), the self enery is also converged and so the jump in
the charge after each dmft1
is much less significant:
0 :CHARGE convergence: 0.3650051
1 :CHARGE convergence: 0.320649
2 :CHARGE convergence: 0.1980197
3 :CHARGE convergence: 0.1882957
4 :CHARGE convergence: 0.1454709
5 :CHARGE convergence: 0.0432447
6 :CHARGE convergence: 0.0190978
7 :CHARGE convergence: 0.0055925
8 :CHARGE convergence: 0.0048235
9 :CHARGE convergence: 0.0024782
10 :CHARGE convergence: 0.0006552
11 :CHARGE convergence: 0.0001947
12 :CHARGE convergence: 0.0002805
13 :CHARGE convergence: 2.05e05
14 :CHARGE convergence: 1.16e05
15 :CHARGE convergence: 1.69e05
16 :CHARGE convergence: 1.84e05
17 :CHARGE convergence: 1.4e06
....
....
51 :CHARGE convergence: 0.0046878
52 :CHARGE convergence: 0.0041113
53 :CHARGE convergence: 0.00243
54 :CHARGE convergence: 0.0013541
55 :CHARGE convergence: 0.0006717
56 :CHARGE convergence: 0.000342
57 :CHARGE convergence: 0.0001287
58 :CHARGE convergence: 3.47e05
59 :CHARGE convergence: 1.37e05
60 :CHARGE convergence: 3.6e06
61 :CHARGE convergence: 0.002555
62 :CHARGE convergence: 0.0022443
63 :CHARGE convergence: 0.0013392
64 :CHARGE convergence: 0.0007663
65 :CHARGE convergence: 0.0003645
66 :CHARGE convergence: 0.0002563
67 :CHARGE convergence: 7.28e05
68 :CHARGE convergence: 1.09e05
69 :CHARGE convergence: 9.6e06
70 :CHARGE convergence: 6.1e06
71 :CHARGE convergence: 6e07
....
....
135 :CHARGE convergence: 0.0002954
136 :CHARGE convergence: 0.0002592
137 :CHARGE convergence: 0.0001538
138 :CHARGE convergence: 0.0001346
139 :CHARGE convergence: 5.24e05
140 :CHARGE convergence: 2.72e05
141 :CHARGE convergence: 6.8e06
142 :CHARGE convergence: 3.2e06
143 :CHARGE convergence: 0.0003925
144 :CHARGE convergence: 0.0003348
145 :CHARGE convergence: 0.0001878
146 :CHARGE convergence: 0.0001563
147 :CHARGE convergence: 4.16e05
148 :CHARGE convergence: 2.61e05
149 :CHARGE convergence: 1.05e05
150 :CHARGE convergence: 2.8e06
Another important file to check is the info.iterate, which contains
information about the chemical potential, the number of electrons on
the V ion, etc:
# #. # mu Vdc Etot Ftot+T*Simp Ftot+T*Simp n_latt n_imp Eimp[0] Eimp[1]
0 0. 0 6.531263 37.772879 2467.797924 2467.812926 2467.796784 4.832377 5.013534 0.054065 0.045105
1 0. 1 6.531263 37.772879 2467.795198 2467.839362 2467.794737 4.856478 5.013534 0.054065 0.045105
2 0. 2 6.531263 37.772879 2467.790068 2467.919012 2467.791341 4.928763 5.013534 0.054065 0.045105
3 0. 3 6.531263 37.772879 2467.805037 2468.148462 2467.807211 5.119680 5.013534 0.054065 0.045105
4 0. 4 6.531263 37.772879 2467.806312 2468.041367 2467.803524 4.975357 5.013534 0.054065 0.045105
5 0. 5 6.531263 37.772879 2467.797455 2468.058270 2467.797604 4.989835 5.013534 0.054065 0.045105
6 0. 6 6.531263 37.772879 2467.795467 2468.037324 2467.795559 4.964081 5.013534 0.054065 0.045105
7 0. 7 6.531263 37.772879 2467.795670 2468.039345 2467.795727 4.967218 5.013534 0.054065 0.045105
8 0. 8 6.531263 37.772879 2467.795453 2468.037709 2467.795553 4.968465 5.013534 0.054065 0.045105
9 0. 9 6.531263 37.772879 2467.795395 2468.034744 2467.795412 4.970982 5.013534 0.054065 0.045105
10 0. 10 6.531263 37.772879 2467.794961 2468.029487 2467.794965 4.974418 5.013534 0.054065 0.045105
11 0. 11 6.531263 37.772879 2467.794815 2468.027650 2467.794804 4.974379 5.013534 0.054065 0.045105
12 0. 12 6.531263 37.772879 2467.794816 2468.027676 2467.794805 4.974472 5.013534 0.054065 0.045105
13 0. 13 6.531263 37.772879 2467.794813 2468.027656 2467.794803 4.974440 5.013534 0.054065 0.045105
14 0. 14 6.531263 37.772879 2467.794813 2468.027658 2467.794803 4.974441 5.013534 0.054065 0.045105
15 0. 15 6.531263 37.772879 2467.794813 2468.027657 2467.794803 4.974440 5.013534 0.054065 0.045105
16 0. 16 6.531263 37.772879 2467.794813 2468.027655 2467.794803 4.974440 5.013534 0.054065 0.045105
17 1. 0 6.531263 37.940316 2467.791665 2467.796323 2467.793875 5.101315 5.033536 0.240213 0.362603
18 1. 1 6.531263 37.940316 2467.791016 2467.781532 2467.792627 5.089714 5.033536 0.240213 0.362603
19 1. 2 6.531263 37.940316 2467.789431 2467.737004 2467.789185 5.054946 5.033536 0.240213 0.362603
20 1. 3 6.531263 37.940316 2467.787966 2467.669527 2467.784865 5.006160 5.033536 0.240213 0.362603
21 1. 4 6.531263 37.940316 2467.786916 2467.648442 2467.783808 5.018529 5.033536 0.240213 0.362603
22 1. 5 6.531263 37.940316 2467.785769 2467.646216 2467.783363 5.028555 5.033536 0.240213 0.362603
23 1. 6 6.531263 37.940316 2467.785657 2467.641236 2467.783057 5.024862 5.033536 0.240213 0.362603
24 1. 7 6.531263 37.940316 2467.785486 2467.638166 2467.782886 5.025596 5.033536 0.240213 0.362603
25 1. 8 6.531263 37.940316 2467.785492 2467.638303 2467.782892 5.025582 5.033536 0.240213 0.362603
26 1. 9 6.531263 37.940316 2467.785489 2467.638261 2467.782888 5.025539 5.033536 0.240213 0.362603
27 1. 10 6.531263 37.940316 2467.785488 2467.638250 2467.782887 5.025538 5.033536 0.240213 0.362603
28 1. 11 6.531263 37.940316 2467.785489 2467.638264 2467.782888 5.025541 5.033536 0.240213 0.362603
29 2. 0 6.531263 37.919732 2467.784434 2467.789381 2467.784203 5.024368 5.031140 0.077242 0.205319
30 2. 1 6.531263 37.919732 2467.784453 2467.790595 2467.784272 5.025296 5.031140 0.077242 0.205319
31 2. 2 6.531263 37.919732 2467.784511 2467.794236 2467.784480 5.028077 5.031140 0.077242 0.205319
....
....
153 14. 6 6.531263 37.919319 2467.784809 2467.786146 2467.784766 5.031462 5.031090 0.090639 0.218392
154 14. 7 6.531263 37.919319 2467.784806 2467.786104 2467.784764 5.031478 5.031090 0.090639 0.218392
155 14. 8 6.531263 37.919319 2467.784806 2467.786091 2467.784763 5.031482 5.031090 0.090639 0.218392
156 14. 9 6.531263 37.919319 2467.784805 2467.786087 2467.784763 5.031482 5.031090 0.090639 0.218392
157 15. 0 6.531263 37.917886 2467.784779 2467.789688 2467.784788 5.031145 5.030921 0.085861 0.213710
158 15. 1 6.531263 37.917886 2467.784780 2467.789750 2467.784791 5.031194 5.030921 0.085861 0.213710
159 15. 2 6.531263 37.917886 2467.784784 2467.789936 2467.784802 5.031343 5.030921 0.085861 0.213710
160 15. 3 6.531263 37.917886 2467.784789 2467.790227 2467.784820 5.031569 5.030921 0.085861 0.213710
161 15. 4 6.531263 37.917886 2467.784792 2467.790226 2467.784820 5.031496 5.030921 0.085861 0.213710
162 15. 5 6.531263 37.917886 2467.784793 2467.790225 2467.784820 5.031493 5.030921 0.085861 0.213710
163 15. 6 6.531263 37.917886 2467.784793 2467.790227 2467.784821 5.031488 5.030921 0.085861 0.213710
164 15. 7 6.531263 37.917886 2467.784794 2467.790230 2467.784821 5.031487 5.030921 0.085861 0.213710
The second column counts the number of DMFT steps (impurity solver),
the third counts charge steps at each DMFT steps, next columns shows
the chemical potential, the fifth column shows the doublecounting
potential, the sixth column shows total energy, followed by two
different estimates of the free energy. The seventh column estimate of
the free energy is not precise, instead the eight column should be
used. [Notice that all quantities, except the total energy and
the free energy are give in eV. The total energies and free energies
are give in Ry, to be compatible with wien2k].
The ninth and tenth column show the impurity occupation
computed from the solid (n_latt) and from the impurity (n_imp).
The last two columns show the impurity levels.
The chemical potential in this insulating materials is fixed. The impurity occupation,
computed from the solid, is quite off at the beginning, however, it becomes
close to nominal 5.0 is just a few iterations. When the calculation is
converged, n_latt and n_imp should match.
Very many steps appear in info.iterate file, and sometimes we
just want to print one line per each dmft steps (the step at which the
charge is converged, and just before the selfenergy is updated). This would correspond to step
16,28,... in the above example.
We can execute
to list only these dmft steps. We can then plot the lattice and
impurity occupation, which should look like this:
Within 6 dmft iterations, the charge is converged to 5.032, and we can
now increase the precision of the Monte Carlo steps. For example,
increase M in params.dat file for factor of 45 to have
much more precise statistics in the last few iterations.
Notice that each variable in params.dat file can be
changed during the run, and it will be updated during the run.
Within 10 or so dmft iterations (around 120 charge iterations), the total energy and the free energy are
converged within 1meV (using 216 cores for impurity solver), as seen here:
Notice that total energy (or free energy) would converge better only
if Monte Carlo statistics is increase, as resulting noise is due to MC noise.
Other files to monitor the run include
dmft_info.out  prints what is being executed, and what were parameters
MnO.scf  like in dft, contains energies and forces and lots of other info
dmft1_info.out  progress of dmft1 step
MnO.outputdmf1  log file of the dmft1 step. Contains the density matrix in :NCOR and the matrix of impurity levels.
dmft2_info.out  progress of dmft2 step
MnO.outputdmf2  log file of the dmft2 step (similar to MnO.output2 from lapw2); contains energies and forces.
MnO.dlt1*  the hybridization function
MnO.gc1*  the local green's function
imp.0/ctqmc.log*  log file of the impurity solver
imp.0/nohup_imp.out.xxx  progress of the impurity solver for each mpi process
Delta.inp*  input hybridization function
imp.0/histogram.dat  the histogram of the perturbation order, i.e., how many kinks does the time evolution contain
Even though the impurity occupations and total energy
converged, we should check the spectral functions to decide if the run
is properly converged. In a metal, it is best to look at the
selfenergy (impurity selfenergies are at : imp.0/Sig.out*,
while the lattice selfenergy, which is just a combination of impurity
ones, are at: sig.inp*).
In a Mott insulator, the selfenergy on real axis has a pole inside the gap, and
therefore its shape on imaginary axis is very sensitive to the relative position of the
chemical potential an the pole. If one accidentally hits the pole by
placing mu on it, the selfenergy on imaginary axis diverges at zero frequency. If
however the chemical potential is slightly moved away from the pole,
selfenergy just becomes large at finite frequencies, but its
imaginary part vanishes at zero. This is because on real axis the imaginary part
vanishes inside the gap, except for the pole. The real part is large,
but not diverging, except when chemical potential is at the pole. The
shape of the selfenergy on imaginary axis is therefore not very
illustrative.
We will therefore rather concentrate on the Green's function, of which
the imaginary part needs to vanish at zero frequency (both on the real and
the imaginary axis). Here is the plot of the impurity Green's function
with iterations:
We see that the green's function is metallic only at the first
iteration, while at the second iteration is almost converged. Such
rapid convergence is here due to the robustness of the gap in the
highspin state on Mn.
We should always check that the impurity and the local green's
function of the solid match. This is a very nontrivial check of the
correctness of the calculation, as the algorithm equates only the
selfenergy at each iteration, while the Green's functions differ
until the selfconsistency is achieved. We see that at the sixth
dmft iteration, the two Green's functions match very well:
Notice that the last Green's functions are named imp.0/Gf.out
and MnO.gc1, while each previous iteration is saved under the
same name, with consecutive number attached to it. The first number
stands for the number of the outside loop, while the second number
enumerates the steps inside the dmft loop (red loop in the figure
above). Since we have max_dmft_iterations=1, the second number
is always 1. The first number would go from 1 to the parameter
finish from params.dat file, or less, if the convergence is
achieved earlier
We see that for practical purposes, the convergence is achieved around
67th iteration, and the next steps are within the statistical noise
identical. The job would not finish though, because by default (to be
cautious) the convergence criteria are set very high. It is therefore
desirable to kill the job, once the overal convergence is obviously
achieved.
Tutorial 1.d. : Postprocessing, analytic continuation, DOS
At this point, we can proceed to the next step of plotting spectra on
the real axis.
Once the calculation is done on the imaginary axis, we need
to do analytical continuation to obtain the self energy on the real
axis. For this, we first take average of the sig.inp files from the
last few steps (in order to reduce the noise) by
saverage.py saverage.py sig.inp.1[05].1
The output is written to sig.inpx. We could append o
option, if we wanted to change this name.
All python scripts include a short help, so by executing for example
it prints the options for this script.
Next, create a new directory (lets call it maxent), and copy the averaged self energy
sig.inpx into it. Also copy
the maxent_params.dat file,
which contains the parameters for the analytical continuation
process:
params={'statistics': 'fermi', # fermi/bose
'Ntau' : 300, # Number of time points
'L' : 20.0, # cutoff frequency on real axis
'x0' : 0.005, # low energy cutoff
'bwdth' : 0.004, # smoothing width
'Nw' : 300, # number of frequency points on real axis
'gwidth' : 2*15.0, # width of gaussian
'idg' : 1, # error scheme: idg=1 > sigma=deltag ; idg=0 > sigma=deltag*G(tau)
'deltag' : 0.004, # error
'Asteps' : 4000, # anealing steps
'alpha0' : 1000, # starting alpha
'min_ratio' : 0.001, # condition to finish, what should be the ratio
'iflat' : 1, # iflat=0 : constant model, iflat=1 : gaussian of width gwidth, iflat=2 : input using file model$
'Nitt' : 1000, # maximum number of outside iterations
'Nr' : 0, # number of smoothing runs
'Nf' : 40, # to perform inverse Fourier, high frequency limit is computed from the last Nf points
}
Perhaps the most important is mesh on the real axis, which is given by
the upper cutoff L, the low energy cutoff x0 and
Nw points. The mesh is nonuniform and more dense at low energy
(tanmesh).
The MC error is here set to 0.004, and can be estimated from the
variation in sig.inp.? files. Notice that we do not continue
the selfenergy, which would have very large error, and would not be
stable to continue analytically. We will analytically continue an auxiliary
function, which is constructed as
and then perform KramersKronig on imaginary part of this function:
and finally invert it to solve for the selfenergy on the real
axis
The script which does that is called maxent_run.py. We
will thus run
which uses the
maximum entropy method to do analytical continuation. This creates the
self energy Sig.out on the real axis. It should be similar to this plot:
Notice that both orbitals have Motttype gap, as there is a pole in
the gap. The sizes of the two gaps are different, with the eg orbitals
having somewhat larger (4.8eV) and t2g smaller (2.7eV) gap.
Now we need to make one last dmft1 calculation in order to obtain the
Green's function and DOS on the real axis. Create a new directory
inside your output directory, and while it the new directory, use
to copy necessary files from the output of the DMFT run to the new directory. Also copy the
analytically continued self energy Sig.out to sig.inp in this new
directory:
cp ../maxent/Sig.out sig.inp
Since we need to run the code on the real axis, we need to
change the NiO.indmfl file. The first number on the second line
of NiO.indmfl file determines whether the code is run on the real axis (0) or the
imaginary axis (1). Change it to 0. The header of MnO.indmfl
file should now look like
5 15 1 5 # hybridization band index nemin and nemax, renormalize for interstitials, projection type
0 0.025 0.025 200 3.000000 1.000000 # matsubara, broadeningcorr, broadeningnoncorr, nomega, omega_min, omega_max (in eV)
1 # number of correlated atoms
1 1 0 # iatom, nL, locrot
2 7 1 # L, qsplit, cix
Then, inside the new directory run
x lapw0 f MnO
x_dmft.py lapw1
x_dmft.py dmft1
Before you might want to set OMP_NUM_THREADS to a large number (but
smaller then the number of cores on your node), such as 10, to
parallelize some loops with open_mp.
Once the run is done, we now have the densities of states on
the real axis. Both the total (column 2) and partial Mnd (column 3)
are stored in MnO.cdos:
To obtain this plot, we increased the number of kpoints to
10,000. The procedure is the same as above: rerun x kgen f MnO
and when asked enter 10000. Then rerun lapw1 and dmft1
step.
Notice that the first excitation into the valence band has roughly
1/3 of dcharacter, and 2/3 of the oxygen character. Notice also that
this is not the real lower Hubbard band within our description, as U
is too large (9eV). This peak is than due to screening of the Mn large
spin by oxygen degrees of freedom, and is a type of Kondo effect. It
is interesting to point out that even in an insulator the Kondo effect
is not completely absent, as such screening occurs at the first
possible excitation. In literature, such effect is also called
ZhangRice singlet, as it was first discussed in cuprates by Zhang & Rice.
Lets note that the first excitation into the conduction band is of
itinerant character, mostly 4s character, hence the scattering rate
for this state is very small, and this state has very high mobility
despite the correlated nature of the gap. Due to finite kpoint mesh,
this DOS appears somewhat spiky.
We can also take a look at the imaginary part of the green's
function. Recall that the imaginary part of the green's function,
divided by pi, gives the DOS. So, plotting the evennumbered columns
in the MnO.gc1 one, we can get the DOS of individual d orbitals (eg and
t2g). The plot is
Notice that column 3 is the eg, and column 5 the t2g DOS.
In this plot it is even more clear that the lower Hubbard band is below
4eV, and that the first valence excitation of the egstates is split
away, making a singlet between the oxygen states and the two eg
orbitals.
Notice that it is exactly this type of screening (hybridization screening) which reduces the low
energy Coulomb interaction, and therefore if one simulates a Hubbard
model for MnO, one needs to take much smaller U. Within our embedded
method, the Coulomb repulsion will thus always be large compared to
model calculations, as such screening very efficiently reduces
interaction in the solid.
Note that the imaginary part of green's function and DOS use slightly
different normalization, therefore the sum of all green's functions
will typically produce slightly larger DOS than is computed in
case.cdos. This is because Green's functions are computed with
normalized projectors within a mufinthin sphere (the integral of
spectral function is by construction exactly unity), while partial dos
usually uses unnormalized projector.
Another quantity of physical interest is the impurity
hybridization function. It is stored in the file MnO.dlt1, and its
imaginary part, which has the units of eV,
looks like the following:
The large peak below the Fermi level signals the hybridization of
eg states with the oxygen p states, as expected on the basis of
previous discussion.