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Third tutorial

Crystalline silicon.

This tutorial aims at showing you how to get the following physical properties, for an insulator:

  • the total energy,
  • the lattice parameter,
  • the band structure (actually, the Kohn-Sham band structure).

You will learn about the use of k-points, as well as the smearing of the plane-wave kinetic energy cut-off.

You will also use the powerful visualisation procedures that have been developed in the Abipy context, relying on matplotlib. See the README of Abipy and the Abipy tutorials. Other visualization tools are available, but will not be covered.

This tutorial should take about 1 hour.


Supposing you made your own installation of ABINIT, the input files to run the examples are in the ~abinit/tests/ directory where ~abinit is the absolute path of the abinit top-level directory. If you have NOT made your own install, ask your system administrator where to find the package, especially the executable and test files.

In case you work on your own PC or workstation, to make things easier, we suggest you define some handy environment variables by executing the following lines in the terminal:

export ABI_HOME=Replace_with_absolute_path_to_abinit_top_level_dir # Change this line
export PATH=$ABI_HOME/src/98_main/:$PATH      # Do not change this line: path to executable
export ABI_TESTS=$ABI_HOME/tests/             # Do not change this line: path to tests dir
export ABI_PSPDIR=$ABI_TESTS/Psps_for_tests/  # Do not change this line: path to pseudos dir

Examples in this tutorial use these shell variables: copy and paste the code snippets into the terminal (remember to set ABI_HOME first!) or, alternatively, source the script located in the ~abinit directory:

source ~abinit/

The ‘export PATH’ line adds the directory containing the executables to your PATH so that you can invoke the code by simply typing abinit in the terminal instead of providing the absolute path.

To execute the tutorials, create a working directory (Work*) and copy there the input files of the lesson.

Most of the tutorials do not rely on parallelism (except specific tutorials on parallelism). However you can run most of the tutorial examples in parallel with MPI, see the topic on parallelism.

Computing the total energy of silicon at a fixed number of k-points

Before beginning, you might consider working in a different subdirectory, as for tutorial 1 or 2. Why not Work3?

The following commands will move you to your working directory, create the Work3 directory, and move you into that directory as you did in the first and second tutorials. Then, we copy the file tbase3_1.abi inside the Work3 directory. The commands are:

cd $ABI_TESTS/tutorial/Input
mkdir Work3
cd Work3
cp ../tbase3_1.abi .

This is your input file:

You should edit it, read it carefully, have a look at the following new input variables and their explanation:

Note also the following: you will work at fixed ecut (12Ha). It is implicit that in real life, you should do a convergence test with respect to ecut. Here, a suitable ecut is given to you; it corresponds to the suggested ecut for this pseudopotential according to the PseudoDojo website where the silicon pseudopotential was taken. When we will relax the lattice parameter, it will result in a lattice parameter that is 0.2% off of the experimental value. Such convergence study has to be made for each physical property that is the target of your interest. While the value of ecut giving converged properties usually do not depend much on the property, this is not true for the convergence with respect to k points.

When you have read the input file, you can run the code, as usual:

abinit tbase3_1.abi > log 2> err &

Then, read the output file, and note the total energy:

etotal   -8.5187390642E+00

Starting the convergence study with respect to k-points

There is, of course, a convergence study associated with the sampling of the Brillouin zone. You should examine different grids, of increasing resolution. You might try the following series of grids:

ngkpt1  2 2 2
ngkpt2  4 4 4
ngkpt3  6 6 6
ngkpt4  8 8 8

However, the associated number of k-points in the irreducible Brillouin zone grows very fast. It is

nkpt1  2
nkpt2 10
nkpt3 28
nkpt4 60

Abinit computes automatically this number of k-points, from the definition of the grid and the symmetries. You might nevertheless define an input nkpt value in the input file, in which case the code will compare its computed value (from the grid) with this input value.

We take this opportunity to examine the behaviour of abinit when a problem is detected. Let us suppose that with ngkpt 4 4 4, one mentions nkpt 2. The input file tbase3_2.abi is an example:

The message that you get a few dozen of lines before the end of the log file is:

--- !BUG
src_file: m_kpts.F90
src_line: 1417
mpi_rank: 0
message: |
    The argument nkpt = 2, does not match
    the number of k points generated by kptopt, kptrlatt, shiftk,
    and the eventual symmetries, that is, nkpt= 10.
    However, note that it might be due to the user,
    if nkpt is explicitely defined in the input file.
    In this case, please check your input file.

  Action: contact ABINIT group (please attach the output of `abinit -b`)

This is a typical abinit error message. It states what is the problem that causes the code to stop, then suggests that it might be due to an error in the input file, namely, an erroneous value of nkpt. The expected value, nkpt 10 is mentioned before the notice that the input file might be erroneous. Then, the file at which the problem occurred is mentioned, as well as the number of the line in that file.

As the computation of nkpt for specific grids of k-points is not an easy task, while the even more important selection of specific economical grids (the best ratio between the accuracy of the integration in the Brillouin zone and the number of k-points) is more difficult, some help to the user is provided by ABINIT.

The code is able to examine automatically different k-point grids, and to propose the best grids for integration. This is described in the abinit help file, see the input variable prtkpt, and the associated characterisation of the integral accuracy, described in kptrlen.


The generation of lists of k-point sets is done in different test cases, in $ABI_TESTS/v2. You can directly have a look at the output files in $ABI_TESTS/v2/Refs, the output files for the tests 61 to 73.

When one begins the study of a new material, it is strongly advised to examine first the list of k-points grids, and select (at least) three efficient ones, for the k-point convergence study.

Do not forget that the CPU time will be linearly proportional to the number of k-points to be treated: using 10 k-points will take five times more than using 2 k-points. Even for a similar accuracy of the Brillouin zone integration (that is to say for about the same value of kptrlen), there might be a grid that will reduce to 10 k-points in the irreducible Brillouin zone and another that will reduce to 2 k-points in the irreducible Brillouin zone. The latter is clearly to be preferred from a computational perspective!

Convergence study with respect to k-points

In order to understand k-point grids, you should read [Monkhorst1976]. Well, maybe not immediately. In the meantime, you can try the above-mentioned convergence study.

The input file tbase3_3.abi is an example, while $ABI_TESTS/tutorial/Refs/tbase3_3.abo is a reference output file.

cd $ABI_TESTS/tutorial/Work3
cp ../tbase3_3.abi .

In this output file, you should have a look at the echo of input variables. As you know, these are preprocessed, and, in particular, ngkpt and shiftk are used to generate the list of k-points (kpt) and their weights (wtk). You should read the information about kpt and wtk.

From the output file, here is the evolution of total energy for the different k-point grids:

etotal1    -8.5187390642E+00
etotal2    -8.5250179735E+00
etotal3    -8.5251232908E+00
etotal4    -8.5251270559E+00

The difference between dataset 3 and dataset 4 is rather small. Even the dataset 2 gives an accuracy of about 0.0001 Ha. So, our converged value for the total energy, at fixed acell, fixed ecut, is -8.8251 Ha.


ABINIT never outputs the value of input variable ngkpt, but instead uses kptrlatt, a 3x3 matrix of integers. In the simplest case, with nshiftk=1, kptrlatt will simply be a diagonal matrix with diagonal values equal to the input ngkpt. However, if nshiftk is not 1, but the combination of ngkpt and shiftk allows ABINIT to generate an homogeneous k point grid with different basis vercotr in reciprocal space, nshiftk might be reduced to 1, and kptrlatt will not be a simple diagonal matrix. Nevertheless, both the input and the echoed grids are equivalent.

Determination of the lattice parameters

The input variable optcell governs the automatic optimisation of cell shape and volume. For the automatic optimisation of cell volume in this cubic crystal, use:

optcell 1
ionmov 2
ntime 10
dilatmx 1.05
ecutsm 0.5

You should read the indications about optcell, dilatmx and ecutsm. In particular, while optcell is adequate for cubic crystals, for the majority of materials, the optimal geometry must include deformations of the cell shape, not simply global rescaling, so the most usual value of optcell to be used is 2. Do not test all the k-point grids, only those with nkpt 2 and 10.

The input file $ABI_TESTS/tutorial/Input/tbase3_4.abi is an example,

while $ABI_TESTS/tutorial/Refs/tbase3_4.abo is a reference output file.

You should obtain the following evolution of the lattice parameters:

acell1     1.0208746777E+01  1.0208746777E+01  1.0208746777E+01 Bohr
acell2     1.0195482058E+01  1.0195482058E+01  1.0195482058E+01 Bohr

with the following very small residual stresses:

strten1    -2.0279878345E-08 -2.0279878345E-08 -2.0279878345E-08
            0.0000000000E+00  0.0000000000E+00  0.0000000000E+00
strten2    -9.2829783285E-08 -9.2829783286E-08 -9.2829783286E-08
            0.0000000000E+00  0.0000000000E+00  0.0000000000E+00

The stress tensor is given in Hartree/Bohr3, and the order of the components is:

                        11  22  33
                        23  13  12

There is only a 0.13% relative difference between acell1 and acell2. So, our converged LDA value for Silicon, with the Si.psp8 pseudopotential of the PseudoDojo website is 10.195 Bohr, that is 5.395 Angstrom. The experimental value is 5.431 Angstrom at 25 degree Celsius, see R.W.G. Wyckoff, Crystal structures Ed. Wiley and sons, New-York (1963) or the NIST database.

Computing the band structure

For the computation of the electronic band structurre, we fix the parameters acell to the theoretical value of 3 * 10.195 (we might also examine the band structure at the experimental value - it is the user’s choice). We use the same ecut than the one used to determine this theoretical value. It is implicit that you should check the sensitivity of the electronic band structure with respect to ecut. Perhaps you will need to raise the value of ecut to obtain the electronic band structure at your target precision. In general, however, as Kohn-Sham band structure suffer inherently of the DFT band gap problem with usual XC functionals, the common practice is to use the same ecut value as the one used for detemining the lattice parameters. We fix also the grid of k-points (the 4x4x4 FCC grid, equivalent to a 8x8x8 Monkhorst-pack grid). The same sensitivity check of the electronic band structure with respect to the choice of grid of k-points should also be made.

We will ask for 8 bands (4 valence and 4 conduction). Whether this choice of conduction band number is adequate depends on the range of energy above the highest occupied state that you want to analyze/represent. In the next section, we will criticize this choice.

A band structure can be computed by solving the Kohn-Sham equation for many different k-points, along different segments of the Brillouin zone. The potential that enters the Kohn-Sham must be derived from a previous self-consistent calculation, and will not vary during the scan of different k-point lines.

Suppose that you want to make a L-Gamma-X-(U-)Gamma circuit, with at least 10 divisions for each segment. The circuit will be obtained easily by the following choice of segment end points:

L     (1/2 0 0)
Gamma (0 0 0)
X     (0 1/2 1/2)
Gamma (1 1 1)

In order to enforce at least 10 divisions for each segment, one uses the input variable ndivsm. ABINIT will generate roughly the same distance between points along each segment, despite each segment having a different length in reciprocal space.


  1. the last Gamma point is in another cell of the reciprocal space than the first one, this choice allows to construct the X-U-Gamma segment easily;

  2. the k-points are specified using reduced coordinates - in agreement with the input setting of the primitive 2-atom unit cell - in standard textbooks, you will often find the L, Gamma or X point given in coordinates of the conventional 8-atom cell: the above-mentioned circuit is then (½ ½ ½)-(0 0 0)-(1 0 0)-(1 1 1), but such (conventional) coordinates cannot be used with the 2-atom (non-conventional) cell.

So, you should set up in your input file, for the first dataset, a usual SCF calculation in which you output the density (prtden 1), and, for the second dataset:

  • fix iscf to -2, to make a non-self-consistent calculation,
  • define getden -1, to take the output density of dataset 1,
  • set nband to 8,
  • set kptopt to -3, to define three segments in the brillouin Zone;
  • set ndivsm to 10,
  • set kptbounds to

    0.5  0.0  0.0 # L point
    0.0  0.0  0.0 # Gamma point
    0.0  0.5  0.5 # X point
    1.0  1.0  1.0 # Gamma point in another cell
  • set enunit to 1, in order to have eigenenergies in eV,

  • the only tolerance criterion admitted for non-self-consistent calculations is tolwfr. You should set it to 1.0d-10 (or so), and suppress toldfe.
  • The nstep parameter was set to 20 to make sure convergence can be reached.

The input file $ABI_TESTS/tutorial/Input/tbase3_5.abi is an example,

while $ABI_TESTS/tutorial/Refs/tbase3_5.abo is a reference output file.

You should find the band structure starting at (second dataset):

Eigenvalues (   eV  ) for nkpt=  39  k points:
kpt#   1, nband=  8, wtk=  1.00000, kpt=  0.5000  0.0000  0.0000 (reduced coord)
-4.83930   -2.21100    3.66138    3.66138    6.36920    8.18203    8.18203   12.44046
kpt#   2, nband=  8, wtk=  1.00000, kpt=  0.4500  0.0000  0.0000 (reduced coord)
-4.97880   -2.00874    3.67946    3.67946    6.39165    8.20580    8.20580   12.47444
kpt#   3, nband=  8, wtk=  1.00000, kpt=  0.4000  0.0000  0.0000 (reduced coord)
-5.30638   -1.49394    3.73328    3.73328    6.45364    8.26444    8.26444   12.56455
kpt#   4, nband=  8, wtk=  1.00000, kpt=  0.3500  0.0000  0.0000 (reduced coord)
-5.69306   -0.79729    3.82286    3.82286    6.55602    8.33970    8.33970   12.65080

One needs a graphical tool to represent all these data. In a separate file (_EIG), you will find the list of k-points and the eigenenergies (the input variable prteig is set by default to 1).

Even without a graphical tool we will have a quick look at the values at L, \Gamma, X and \Gamma again:

kpt#   1, nband=  8, wtk=  1.00000, kpt=  0.5000  0.0000  0.0000 (reduced coord)
-4.83930   -2.21100    3.66138    3.66138    6.36920    8.18203    8.18203   12.44046

kpt#  11, nband=  8, wtk=  1.00000, kpt=  0.0000  0.0000  0.0000 (reduced coord)
-7.22396    4.87519    4.87519    4.87519    7.42159    7.42159    7.42159    8.26902

kpt#  23, nband=  8, wtk=  1.00000, kpt=  0.0000  0.5000  0.5000 (reduced coord)
-3.01262   -3.01262    1.97054    1.97054    5.46033    5.46033   15.02324   15.02382

kpt#  39, nband=  8, wtk=  1.00000, kpt=  1.0000  1.0000  1.0000 (reduced coord)
-7.22396    4.87519    4.87519    4.87519    7.42159    7.42159    7.42159    8.26902

The last \Gamma is exactly equivalent to the first \Gamma. It can be checked that the top of the valence band is obtained at \Gamma (=4.87519 eV). The width of the valence band is 12.1 eV, the lowest unoccupied state at X is 0.585 eV higher than the top of the valence band, at \Gamma. Note that the zero of eigenenergies is not fixed at the top of the valence band. Instead, the top of the valence band is by default the expectation value of the corresponding eigenfunction for the Kohn-Sham potential with zero average macroscopic Hartree potential (other choices can be made, but this is a topic for experts).

The Si is described as an indirect band gap material (this is correct), with a band-gap of about 0.585 eV (this is quantitatively quite wrong: the experimental value 1.17 eV is at 25 degree Celsius). The minimum of the conduction band is slightly displaced with respect to X, see kpt # 21 (this is correct). This underestimation of the band gap is well-known (the famous DFT band-gap problem). In order to obtain correct band gaps, you need to go beyond the Kohn-Sham Density Functional Theory: use the GW approximation. This is described in the first GW tutorial.

For experimental data and band structure representation, see the book by M.L. Cohen and J.R. Chelikowski [Cohen1988].


There is a subtlety that is worth to comment about. In non-self-consistent calculations, like those performed in the present band structure calculation, with iscf = -2, not all bands are converged within the tolerance tolwfr. Indeed, the two upper bands (by default) have not been taken into account to apply this convergence criterion: they constitute a buffer. The number of such buffer bands is governed by the input variable nbdbuf.

It can happen that the highest (or two highest) band(s), if not separated by a gap from non-treated bands, can exhibit a very slow convergence rate. This buffer allows achieving convergence of important, non-buffer bands. In the present case, 6 bands have been converged with a residual better than tolwfr, while the two upper bands are less converged (still sufficiently for graphical representation of the band structure). In order to achieve the same convergence for all 8 bands, it is advised to use nband=10 (that is, 8 + 2).

Using AbiPy to automate the most boring steps

The AbiPy package provides several tools to facilitate the preparation of band structure calculations and the analysis of the output results. First of all, one can use the script with the kpath command to determine a high-symmetry k-path from any file containing structural information (abinit input file, netcdf output files etc.). The high-symmetry k-path follows the conventions described in [Setyawan2010]. Let’s try with: kpath tbase3_5.abi

# Abinit Structure
 natom 2
 ntypat 1
 typat 1 1
 znucl 14
    0.0000000000    0.0000000000    0.0000000000
    0.2500000000    0.2500000000    0.2500000000
 acell    1.0    1.0    1.0
    0.0000000000    5.1080000000    5.1080000000
    5.1080000000    0.0000000000    5.1080000000
    5.1080000000    5.1080000000    0.0000000000

# K-path in reduced coordinates:
# tolwfr 1e-20 iscf -2 getden ??
 ndivsm 10
 kptopt -11
    +0.00000  +0.00000  +0.00000 # $\Gamma$
    +0.50000  +0.00000  +0.50000 # X
    +0.50000  +0.25000  +0.75000 # W
    +0.37500  +0.37500  +0.75000 # K
    +0.00000  +0.00000  +0.00000 # $\Gamma$
    +0.50000  +0.50000  +0.50000 # L
    +0.62500  +0.25000  +0.62500 # U
    +0.50000  +0.25000  +0.75000 # W
    +0.50000  +0.50000  +0.50000 # L
    +0.37500  +0.37500  +0.75000 # K
    +0.62500  +0.25000  +0.62500 # U
    +0.50000  +0.00000  +0.50000 # X

To visualize the band structure stored in the file, use the script and the command line: --expose -sns=talk

You obtain the visualization of the Brillouin Zone (with the notation for several high-symmetry wavevectors), the representation of the primitive cell with location of atoms, and the sought graphical representation of the electronic band structure.

Concerning the latter, AbiPy has performed a shift of the computed eigenenergies, to have the top of the valence bands approximately at zero. Note that this shift is not the exact one: the top of the valence band that happen at \Gamma is not exactly zero. This is because the computation of the occupied states during the self-consistent DFT calculation was done with a k point grid that did not include the \Gamma point. ABINIT determined the energy of the highest occupied state for the points of that k point grid. The subsequent non-self-consistent calculation of the band eigenenergies, that included calculation at \Gamma, was not associated with occupation numbers. Hence, the value of the top of the valence band could not be corrected. In order to obtain an electronic band structure with the top of the valence band aligned with zero using AbiPy, the corresponding k point must belong to the grid used for self-consistent calculations. However, this point with the highest occupied state is not known a priori.

If this is a mandatory target of the electronic structure representation, the user has the choice. He/she can make his/her own post-treatment of the data contained in the GSR file. Alternatively, for the automatic generation of the correcly aligned band structure using AbiPy, the user should proceed with more steps: after the self-consistent calculation with a particular k point grid, the band structure at high symmetry points is scanned, the k point for which the top of the valence band is obtained is determined, and, if not included in the initial grid for self-consistent calculation, a new self-consistent calculation must be done, with a more appropriate grid. Since the top of the valence bands often happens at \Gamma, one might also from the very start decide to make self-consistent calculations with grids that include \Gamma, although such grids are usually not as efficient as the shifted grids.

Although the electronic band structure obtained with AbiPy looks nice in the above figure, there is a pitfall. Indeed, AbiPy decided to represent the energy range from about -13 eV to about +12 eV, while, as given in the input file above, 4 valence bands and 4 conduction bands were computed. The representation of valence bands is fine, but apparently, there is no electronic state above +10 eV. Is this correct ? Please, perform a calculation of the band structure with many more bands, for example 20 bands. You will observe that the representation of the band structure up to +12 eV obtained with 8 bands is indeed incomplete: more conduction bands connect to the four ones initially represented, and they fill the energy range up to +12 eV, and much beyond. There is no gap between the four conduction bands that had already been computed and the additional higher ones. AbiPy by default chooses a range larger than all the bands that have been computed. So, this pitfall is present by default !

The aim of the user might be to have a quick look at the band structure, in which case the default representation by AbiPy is fine. However, if the user wants to place a band structure in a report, he/she would better cut the band structure from above in order to avoid showing an incomplete set of bands, falsely picturing a gap between the represented conduction bands and those not represented.

It is also possible to compare multiple GSR files with the script and the syntax gsr -e -sns=talk

to produce the following figures:

For further details about the AbiPy API and the GSR file, please consult the GsrFile notebook .