Graphite interlayer distance: Difference between revisions
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== Task == | == Task == | ||
In this example you will determine the interlayer distance of graphite in the stacking direction using the method of Tchatchenko and Scheffler to account for van der Waals interactions. | |||
of Tchatchenko and Scheffler to account for van der Waals interactions. | |||
Semilocal DFT at the GGA level underestimates long-range dispersion interactions. | |||
This problem causes a bad overestimation of graphite lattice in the stacking direction: 8.84 Å (PBE) vs. 6.71 Å (exp). | |||
In this example, the [[Tkatchenko-Scheffler method|dispersion correction method of Tchatchenko and Scheffler]] is used to cope with this problem. | |||
== Input == | == Input == | ||
Line 47: | Line 51: | ||
0 0 0 | 0 0 0 | ||
== | == Running this example == | ||
To run this example, execute the <code>run.sh</code> bash-script: | |||
<pre> | |||
# | |||
# To run VASP this script calls $vasp_std | |||
# (or posibly $vasp_gam and/or $vasp_ncl). | |||
# These variables can be defined by sourcing vaspcmd | |||
. vaspcmd 2> /dev/null | |||
# | |||
# When vaspcmd is not available and $vasp_std, | |||
# $vasp_gam, and/or $vasp_ncl are not set as environment | |||
# variables, you can specify them here | |||
[ -z "`echo $vasp_std`" ] && vasp_std="mpirun -np 8 /path-to-your-vasp/vasp_std" | |||
[ -z "`echo $vasp_gam`" ] && vasp_gam="mpirun -np 8 /path-to-your-vasp/vasp_gam" | |||
[ -z "`echo $vasp_ncl`" ] && vasp_ncl="mpirun -np 8 /path-to-your-vasp/vasp_ncl" | |||
# | |||
# The real work starts here | |||
# | |||
rm results.dat | |||
for d in 6.5 6.6 6.65 6.7 6.75 6.8 6.9 7.0 | |||
do | |||
cat>POSCAR<<! | |||
graphite | |||
1.0 | |||
1.22800000 -2.12695839 0.00000000 | |||
1.22800000 2.12695839 0.00000000 | |||
0.00000000 0.00000000 $d | |||
4 | |||
direct | |||
0.00000000 0.00000000 0.25000000 | |||
0.00000000 0.00000000 0.75000000 | |||
0.33333333 0.66666667 0.25000000 | |||
0.66666667 0.33333333 0.75000000 | |||
! | |||
$vasp_std | |||
cp OUTCAR OUTCAR.$d | |||
energy=$(grep "free ene" OUTCAR.$d|awk '{print $5}') | |||
echo $d $energy >> results.dat | |||
done | |||
</pre> | |||
The optimal length of the lattice vector ''c'' normal to the stacking direction is determined in a series of single point calculations with varied value of ''c'' (all other degrees of freedom are fixed at their experimental values). | |||
of | |||
The computed ''c'' vs. energy dependence is written in the file <code>results.dat</code> and can be visualized e.g. using ''xmgrace''. | |||
The optimal value can be obtained using the attached utility (python with numpy or Numeric is needed): | |||
./utilities/fit.py results.dat | |||
This should yield: | |||
200 iterations performed | 200 iterations performed | ||
Line 79: | Line 113: | ||
d0(A): 6.65603352689 | d0(A): 6.65603352689 | ||
The computed value of 6.66 | The computed value of 6.66 Å agrees well with experiment (6.71 Å). | ||
experiment (6.71 | |||
== Download == | == Download == | ||
[ | [[Media:GraphiteDistance ts.tgz| graphiteDistance_ts.tgz]] | ||
{{Template:Bulk_systems}} | {{Template:Bulk_systems}} | ||
[[Category:Examples]] | [[Category:Examples]] |
Latest revision as of 13:25, 14 November 2019
Task
In this example you will determine the interlayer distance of graphite in the stacking direction using the method of Tchatchenko and Scheffler to account for van der Waals interactions.
Semilocal DFT at the GGA level underestimates long-range dispersion interactions. This problem causes a bad overestimation of graphite lattice in the stacking direction: 8.84 Å (PBE) vs. 6.71 Å (exp).
In this example, the dispersion correction method of Tchatchenko and Scheffler is used to cope with this problem.
Input
POSCAR
graphite 1.0 1.22800000 -2.12695839 0.00000000 1.22800000 2.12695839 0.00000000 0.00000000 0.00000000 7.0 4 direct 0.00000000 0.00000000 0.25000000 0.00000000 0.00000000 0.75000000 0.33333333 0.66666667 0.25000000 0.66666667 0.33333333 0.75000000
INCAR
IVDW = 20 LVDW_EWALD =.TRUE. NSW = 1 IBRION = 2 ISIF = 4 PREC = Accurate EDIFFG = 1e-5 LWAVE = .FALSE. LCHARG = .FALSE. ISMEAR = -5 SIGMA = 0.01 EDIFF = 1e-6 ALGO = Fast NPAR = 2
KPOINTS
Monkhorst Pack 0 gamma 16 16 8 0 0 0
Running this example
To run this example, execute the run.sh
bash-script:
# # To run VASP this script calls $vasp_std # (or posibly $vasp_gam and/or $vasp_ncl). # These variables can be defined by sourcing vaspcmd . vaspcmd 2> /dev/null # # When vaspcmd is not available and $vasp_std, # $vasp_gam, and/or $vasp_ncl are not set as environment # variables, you can specify them here [ -z "`echo $vasp_std`" ] && vasp_std="mpirun -np 8 /path-to-your-vasp/vasp_std" [ -z "`echo $vasp_gam`" ] && vasp_gam="mpirun -np 8 /path-to-your-vasp/vasp_gam" [ -z "`echo $vasp_ncl`" ] && vasp_ncl="mpirun -np 8 /path-to-your-vasp/vasp_ncl" # # The real work starts here # rm results.dat for d in 6.5 6.6 6.65 6.7 6.75 6.8 6.9 7.0 do cat>POSCAR<<! graphite 1.0 1.22800000 -2.12695839 0.00000000 1.22800000 2.12695839 0.00000000 0.00000000 0.00000000 $d 4 direct 0.00000000 0.00000000 0.25000000 0.00000000 0.00000000 0.75000000 0.33333333 0.66666667 0.25000000 0.66666667 0.33333333 0.75000000 ! $vasp_std cp OUTCAR OUTCAR.$d energy=$(grep "free ene" OUTCAR.$d|awk '{print $5}') echo $d $energy >> results.dat done
The optimal length of the lattice vector c normal to the stacking direction is determined in a series of single point calculations with varied value of c (all other degrees of freedom are fixed at their experimental values).
The computed c vs. energy dependence is written in the file results.dat
and can be visualized e.g. using xmgrace.
The optimal value can be obtained using the attached utility (python with numpy or Numeric is needed):
./utilities/fit.py results.dat
This should yield:
200 iterations performed Ch-square: 4.30305519481e-09 --------- E0(eV): -37.433456779 d0(A): 6.65603352689
The computed value of 6.66 Å agrees well with experiment (6.71 Å).