Modelling a gallium arsenide surface
This example shows how to use the atomistic simulation environment or ASE for short, to set up and run a particular calculation of a gallium arsenide surface. ASE is a Python package to simplify the process of setting up, running and analysing results from atomistic simulations across different simulation codes. For more details on the integration DFTK provides with ASE, see Atomistic simulation environment (ASE).
In this example we will consider modelling the (1, 1, 0) GaAs surface separated by vacuum.
Parameters of the calculation. Since this surface is far from easy to converge, we made the problem simpler by choosing a smaller Ecut
and smaller values for n_GaAs
and n_vacuum
. More interesting settings are Ecut = 15
and n_GaAs = n_vacuum = 20
.
miller = (1, 1, 0) # Surface Miller indices
n_GaAs = 2 # Number of GaAs layers
n_vacuum = 4 # Number of vacuum layers
Ecut = 5 # Hartree
kgrid = (4, 4, 1); # Monkhorst-Pack mesh
Use ASE to build the structure:
using ASEconvert
using PythonCall
a = 5.6537 # GaAs lattice parameter in Ångström (because ASE uses Å as length unit)
gaas = ase.build.bulk("GaAs", "zincblende"; a)
surface = ase.build.surface(gaas, miller, n_GaAs, 0, periodic=true);
Get the amount of vacuum in Ångström we need to add
d_vacuum = maximum(maximum, surface.cell) / n_GaAs * n_vacuum
surface = ase.build.surface(gaas, miller, n_GaAs, d_vacuum, periodic=true);
Write an image of the surface and embed it as a nice illustration:
ase.io.write("surface.png", surface * pytuple((3, 3, 1)), rotation="-90x, 30y, -75z")
Python: None

Use the pyconvert
function from PythonCall
to convert the ASE atoms to an AtomsBase-compatible system. This can then be used in the same way as other AtomsBase
systems (see AtomsBase integration for details) to construct a DFTK model:
using DFTK
using PseudoPotentialData
pseudopotentials = PseudoFamily("cp2k.nc.sr.pbe.v0_1.largecore.gth")
model = model_DFT(pyconvert(AbstractSystem, surface);
functionals=PBE(),
temperature=1e-3,
smearing=DFTK.Smearing.Gaussian(),
pseudopotentials)
Model(gga_x_pbe+gga_c_pbe, 3D):
lattice (in Bohr) : [7.55469 , 0 , 0 ]
[0 , 7.55469 , 0 ]
[0 , 0 , 40.0648 ]
unit cell volume : 2286.6 Bohr³
atoms : As₂Ga₂
atom potentials : ElementPsp(Ga, "/home/runner/.julia/artifacts/9a2a5dc89d1b33bff2ad61eaf2d000191050d15c/Ga.gth")
ElementPsp(As, "/home/runner/.julia/artifacts/9a2a5dc89d1b33bff2ad61eaf2d000191050d15c/As.gth")
ElementPsp(Ga, "/home/runner/.julia/artifacts/9a2a5dc89d1b33bff2ad61eaf2d000191050d15c/Ga.gth")
ElementPsp(As, "/home/runner/.julia/artifacts/9a2a5dc89d1b33bff2ad61eaf2d000191050d15c/As.gth")
num. electrons : 16
spin polarization : none
temperature : 0.001 Ha
smearing : DFTK.Smearing.Gaussian()
terms : Kinetic()
AtomicLocal()
AtomicNonlocal()
Ewald(nothing)
PspCorrection()
Hartree()
Xc(gga_x_pbe, gga_c_pbe)
Entropy()
In the above we use the pseudopotential
keyword argument to assign the respective pseudopotentials to the imported model.atoms
. Try lowering the SCF convergence tolerance (tol
) or try mixing=KerkerMixing()
to see the full challenge of this system.
basis = PlaneWaveBasis(model; Ecut, kgrid)
scfres = self_consistent_field(basis; tol=1e-6, mixing=LdosMixing());
n Energy log10(ΔE) log10(Δρ) Diag Δtime
--- --------------- --------- --------- ---- ------
1 -16.58755966358 -0.58 5.3 436ms
2 -16.72504937392 -0.86 -1.01 1.0 240ms
3 -16.73048473609 -2.26 -1.57 2.0 263ms
4 -16.73122209339 -3.13 -2.16 1.0 238ms
5 -16.73132283580 -4.00 -2.58 2.0 250ms
6 -16.73133178951 -5.05 -2.83 1.9 234ms
7 -16.73069023752 + -3.19 -2.40 2.3 264ms
8 -16.73131024298 -3.21 -2.89 2.2 245ms
9 -16.73125379305 + -4.25 -2.82 2.0 245ms
10 -16.73132738071 -4.13 -3.22 1.6 217ms
11 -16.73133761079 -4.99 -3.54 1.1 888ms
12 -16.73133896353 -5.87 -3.67 1.0 191ms
13 -16.73133972487 -6.12 -3.87 1.0 197ms
14 -16.73134016436 -6.36 -4.39 1.7 214ms
15 -16.73134016540 -8.98 -4.49 2.1 249ms
16 -16.73134018589 -7.69 -4.65 1.0 194ms
17 -16.73134019862 -7.90 -4.93 1.0 198ms
18 -16.73134020035 -8.76 -5.26 1.7 211ms
19 -16.73134020031 + -10.39 -5.32 1.8 233ms
20 -16.73134020035 -10.39 -5.53 1.1 204ms
21 -16.73134020042 -10.17 -5.88 1.9 232ms
22 -16.73134020042 + -11.70 -6.07 1.9 244ms
scfres.energies
Energy breakdown (in Ha):
Kinetic 5.8593963
AtomicLocal -105.6100002
AtomicNonlocal 2.3494805
Ewald 35.5044300
PspCorrection 0.2016043
Hartree 49.5614156
Xc -4.5976632
Entropy -0.0000035
total -16.731340200421