Graphite interlayer distance: Difference between revisions
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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. | 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 [[Tkatchenko-Scheffler method|dispersion correction method of Tchatchenko and Scheffler]] is used to cope with this problem. | |||
== Input == | == Input == |
Revision as of 21:18, 24 June 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
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
200 iterations performed Ch-square: 4.30305519481e-09 --------- E0(eV): -37.433456779 d0(A): 6.65603352689
The computed value of 6.66 A agrees well with experiment (6.71 A).
Download
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