Introduction¶
GPAW calculations are controlled through scripts written in the programming language Python. GPAW relies on the Atomic Simulation Environment (ASE), which is a Python package that helps us describe our atoms. The ASE package also handles molecular dynamics, analysis, visualization, geometry optimization and more. If you don’t know anything about ASE, then it might be a good idea to familiarize yourself with it before continuing (at least read the About section).
Below, there will be Python code examples starting with >>>
(and
...
for continuation lines). It is a good idea to start the
Python interpreter and try some of the examples below.
The units used by the GPAW calculator correspond to the ASE
conventions
, most importantly electron volts and
angstroms.
Doing a PAW calculation¶
To do a PAW calculation with the GPAW code, you need an ASE
Atoms
object and a GPAW
calculator:
_____________ ____________
   
 Atoms > GPAW 
   
_____________ ____________
atoms calc
In Python code, it looks like this:
from ase import Atoms
from gpaw import GPAW
d = 0.74
a = 6.0
atoms = Atoms('H2',
positions=[(0, 0, 0),
(0, 0, d)],
cell=(a, a, a))
atoms.center()
calc = GPAW(mode='fd', nbands=2, txt='h2.txt')
atoms.calc = calc
print(atoms.get_forces())
If the above code was executed, a calculation for a single \(\rm{H}_2\) molecule would be started. The calculation would be done using a supercell of size \(6.0 \times 6.0 \times 6.0\) Å with cluster boundary conditions. The parameters for the PAW calculation are:
2 electronic bands.
Local density approximation (LDA)[1] for the exchangecorrelation functional.
Spinpaired calculation.
\(32 \times 32 \times 32\) grid points.
The values of these parameters can be found in the text output file:
h2.txt
.
The calculator will try to make sensible choices for all parameters that the user does not specify. Specifying parameters can be done like this:
>>> calc = GPAW(mode='fd',
... nbands=1,
... xc='PBE',
... gpts=(24, 24, 24))
Here, we want to use one electronic band, the Perdew, Burke, Ernzerhof (PBE)[2] exchangecorrelation functional and 24 grid points in each direction.
Parameters¶
The complete list of all possible parameters and their defaults is shown below. A detailed description of the individual parameters is given in the following sections.
keyword 
type 
default value 
description 




Specification of Atomic basis set 



Total Charge of the system 

Object 









Object 


seq 










seq 
\(\Gamma\)point 






Object 
Pulay Density mixing scheme 








occ. obj. 





Object 
Specification of Poisson solver or dipole correction or Advanced Poisson solver 




Use random numbers for Wave function initialization 


















seq: A sequence of three int
’s.
Note
Parameters can be changed after the calculator has been constructed
by using the set()
method:
>>> calc.set(txt='H2.txt', charge=1)
This would send all output to a file named 'H2.txt'
, and the
calculation will be done with one electron removed.
Deprecated keywords (in favour of the parallel
keyword) include:
keyword 
type 
default value 
description 


seq 
Parallel Domain decomposition 




Planewave, LCAO or Finitedifference mode?¶
 Planewaves:
Expand the wave functions in planewaves. Use
mode='pw'
if you want to use the default planewave cutoff of \(E_{\text{cut}}=340\) eV. The planewaves will be those with \(\mathbf G+\mathbf k^2/2<E_{\text{cut}}\). You can set another cutoff like this:from gpaw import GPAW, PW calc = GPAW(mode=PW(200))
 LCAO:
Expand the wave functions in a basisset constructed from atomiclike orbitals, in short LCAO (linear combination of atomic orbitals). This is done by setting
mode='lcao'
.See also the page on LCAO Mode.
 Finitedifference:
Expand the wave functions on a real space grid, chosen by
mode='fd'
.
Warning
In the future, it will become an error to not specify a mode parameter for a DFT calculation. For now, users will get a warning when finitedifference mode is implicitly chosen. Please change your scripts to avoid this error/warning.
Comparing PW, LCAO and FD modes¶
 Memory consumption:
With LCAO, you have fewer degrees of freedom so memory usage is low. PW mode uses more memory and FD a lot more.
 Speed:
For small systems with many kpoints, PW mode beats everything else. For larger systems LCAO will be most efficient. Whereas PW beats FD for smallish systems, the opposite is true for large systems where FD will parallelize better.
 Absolute convergence:
With LCAO, it can be hard to reach the complete basis set limit and get absolute convergence of energies, whereas with FD and PW mode it is quite easy to do by decreasing the grid spacing or increasing the planewave cutoff energy, respectively.
 Eggbox errors:
With LCAO and FD mode you will get a small eggbox error: you get a small periodic energy variation as you translate atoms and the period of the variation will be equal to the gridspacing used. GPAW’s PW implementation doesn’t have this problem.
 Features:
FD mode is the oldest and has most features. Only PW mode can be used for calculating the stresstensor and for response function calculations.
Number of electronic bands¶
This parameter determines how many bands are included in the calculation for
each spin. For example, for spinunpolarized system with 10 valence electrons
nbands=5
would include all the occupied states. In 10 valence electron
spinpolarized system with magnetic moment of 2 a minimum of nbands=6
is
needed (6 occupied bands for spinup, 4 occupied bands and 2 empty bands for
spin down).
The default number of electronic bands (nbands
) is equal to 4 plus
1.2 times the number of occupied bands. For systems
with the occupied states well separated from the unoccupied states,
one could use just the number of bands needed to hold the occupied
states. For metals, more bands are needed. Sometimes, adding more
unoccupied bands will improve convergence.
Tip
nbands=0
will give zero empty bands, and nbands=n
will
give n
empty bands.
Tip
nbands='n%'
will give n/100
times the number of occupied bands.
Tip
nbands='nao'
will use the same number of bands as there are
atomic orbitals. This corresponds to the maximum nbands
value that
can be used in LCAO mode.
ExchangeCorrelation functional¶
Some of the most commonly used exchangecorrelation functionals are listed below.

full libxc keyword 
description 
reference 



Local density approximation 



Perdew, Burke, Ernzerhof 



revised PBE 



revised revPBE 



Known as PBE0 



B3LYP (as in Gaussian Inc.) 
'LDA'
is the default value. The next three ones are of
generalized gradient approximation (GGA) type, and the last two are
hybrid functionals.
For the list of all functionals available in GPAW see Exchangecorrelation functionals module.
GPAW uses the functionals from libxc by default
(except for LDA, PBE, revPBE, RPBE and PW91 where GPAW’s own implementation
is used).
Keywords are based on the names in the libxc 'xc_funcs.h'
header
file (the leading 'XC_'
should be removed from those names).
You should be able to find the file installed alongside LibXC.
Valid
keywords are strings or combinations of exchange and correlation string
joined by + (plus). For example, “the” (most common) LDA approximation
in chemistry corresponds to 'LDA_X+LDA_C_VWN'
.
XC functionals can also be specified as dictionaries. This is useful for
functionals that depend on one or more parameters. For example, to use a
stencil with two nearest neighbours for the densitygradient with the PBE
functional, use xc={'name': 'PBE', 'stencil': 2}
. The stencil
keyword applies to any GGA or MGGA. Some functionals may take other
parameters; see their respective documentation pages.
Hybrid functionals (the feature is described at Exact exchange) require the setups containing exx information to be generated. Check available setups for the presence of exx information, for example:
$ zcat $GPAW_SETUP_PATH/O.PBE.gz  grep "<exact_exchange_"
and generate setups with missing exx information:
$ gpawsetup exactexchange f PBE H C
Currently all the hybrid functionals use the PBE setup as a base setup.
For more information about gpawsetup
see Setup generation.
Set the location of setups as described on Installation of PAW datasets.
The details of the implementation of the exchangecorrelation are described on the Exchange and correlation functionals page.
Brillouinzone sampling¶
The default sampling of the Brillouinzone is with only the
\(\Gamma\)point. This allows us to choose the wave functions to be
real. MonkhorstPack sampling can be used if required: kpts=(N1,
N2, N3)
, where N1
, N2
and N3
are positive integers.
This will sample the Brillouinzone with a regular grid of N1
\(\times\) N2
\(\times\) N3
kpoints. See the
ase.dft.kpoints.monkhorst_pack()
function for more details.
For more flexibility, you can use this syntax:
kpts={'size': (4, 4, 4)} # 4x4x4 Monkhorstpack
kpts={'size': (4, 4, 4), 'gamma': True} # shifted 4x4x4 Monkhorstpack
You can also specify the kpoint density in units of points per Å\(^{1}\):
kpts={'density': 2.5} # MP with a minimum density of 2.5 points/Ang^1
kpts={'density': 2.5, 'even': True} # round up to nearest even number
kpts={'density': 2.5, 'gamma': True} # include gammapoint
The kpoint density is calculated as:
where \(N\) is then number of kpoints and \(a\) is the length of the unitcell along the direction of the corresponding reciprocal lattice vector.
An arbitrary set of kpoints can be specified, by giving a sequence of kpoint coordinates like this:
kpts=[(0, 0, 0.25), (0, 0, 0), (0, 0, 0.25), (0, 0, 0.5)]
The kpoint coordinates are given in scaled coordinates, relative to the basis vectors of the reciprocal unit cell.
The above four kpoints are equivalent to
kpts={'size': (1, 1, 4), 'gamma': True}
and to this:
>>> from ase.dft.kpoints import monkhorst_pack
>>> kpts = monkhorst_pack((1, 1, 4))
>>> kpts
array([[ 0. , 0. , 0.375],
[ 0. , 0. , 0.125],
[ 0. , 0. , 0.125],
[ 0. , 0. , 0.375]])
>>> kpts+=(0,0,0.125)
>>> kpts
array([[ 0. , 0. , 0.25],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0.25],
[ 0. , 0. , 0.5 ]])
Spinpolarized calculation¶
If any of the atoms have magnetic moments, then the calculation will
be spinpolarized  otherwise, a spinpaired calculation is carried
out. This behavior can be overruled with the spinpol
keyword
(spinpol=True
).
Number of grid points¶
The number of grid points to use for the grid representation of the
wave functions determines the quality of the calculation. More
gridpoints (smaller grid spacing, h), gives better convergence of
the total energy. For most elements, h should be 0.2 Å for
reasonable convergence of total energies. If a n1
\(\times\) n2
\(\times\) n3
grid is desired, use gpts=(n1, n2, n3)
, where
n1
, n2
and n3
are positive int
’s all divisible by four.
Alternatively, one can use something like h=0.25
, and the program
will try to choose a number of grid points that gives approximately
a gridpoint density of \(1/h^3\). For more details, see Grids.
If you are more used to think in terms of plane waves; a conversion formula between plane wave energy cutoffs and realspace grid spacings have been provided by Briggs et al. PRB 54, 14362 (1996). The conversion can be done like this:
>>> from gpaw.utilities.tools import cutoff2gridspacing, gridspacing2cutoff
>>> from ase.units import Rydberg
>>> h = cutoff2gridspacing(50 * Rydberg)
Grid spacing¶
The parameter h
specifies the grid spacing in Å that has to be
used for the realspace representation of the smooth wave
functions. Note, that this grid spacing in most cases is approximate
as it has to fit to the unit cell (see Number of grid points above).
In case you want to specify h
exactly you have to choose the unit
cell accordingly. This can be achieved by:
from gpaw.cluster import *
d = 0.74
a = 6.0
atoms = Cluster('H2', positions=[(0, 0, 0), (0, 0, d)])
# set the amount of vacuum at least to 4 Å
# and ensure a grid spacing of h=0.2
atoms.minimal_box(4., h=.2)
Use of symmetry¶
The default behavior is to use all pointgroup symmetries and timereversal symmetry to reduce the kpoints to only those in the irreducible part of the Brillouinzone. Moving the atoms so that a symmetry is broken will cause an error. This can be avoided by using:
symmetry={'point_group': False}
This will reduce the number of applied symmetries to just the timereversal symmetry (implying that the Hamiltonian is invariant under k > k). For some purposes you might want to have no symmetry reduction of the kpoints at all (debugging, bandstructure calculations, …). This can be achieved by specifying:
symmetry={'point_group': False, 'time_reversal': False}
or simply symmetry='off'
which is a shorthand notation for the same
thing.
For full control, here are all the available keys of the symmetry
dictionary:
key 
default 
description 



Use pointgroup symmetries 


Use timereversal symmetry 


Use only symmorphic symmetries 


Relative tolerance 
Wave function initialization¶
By default, a linear combination of atomic orbitals is used as initial guess for the wave functions. If the user wants to calculate more bands than there are precalculated atomic orbitals, random numbers will be used for the remaining bands.
Occupation numbers¶
The smearing of the occupation numbers is controlled like this:
from gpaw import GPAW
calc = GPAW(...,
occupations={'name': 'fermidirac',
'width': 0.05},
...)
The distribution looks like this (width = \(k_B T\)):
For calculations with periodic boundary conditions, the default value
is 0.1 eV and the total energies are extrapolated to T = 0 Kelvin.
For a molecule (no periodic boundaries) the default value is width=0
,
which gives integer occupation numbers.
Other distribution functions:
{'name': 'marzarivanderbilt', 'width': ...}
{'name': 'methfesselpaxton', 'width': ..., 'order': ...}
For a spinpolarized calculation, one can fix the total magnetic moment at the initial value using:
occupations={'name': ..., 'width': ..., 'fixmagmom': True}
For fixed occupations numbers use the
gpaw.occupations.FixedOccupationNumbers
class like this:
from gpaw.occupations import FixedOccupationNumbers
calc = GPAW(...,
occupations=FixedOccupationNumbers([[1, 1, ..., 0, 0],
[1, 1, ..., 0, 0]]))
See also Occupation number smearing.
Compensation charges¶
The compensation charges are expanded with correct multipoles up to
and including \(\ell=\ell_{max}\). Default value: lmax=2
.
Charge¶
The default is charge neutral. The systems total charge may be set in
units of the negative electron charge (i.e. charge=1
means one
electron more than the neutral).
Accuracy of the selfconsistency cycle¶
The convergence
keyword is used to set the convergence criteria
for the SCF cycle.
The default value is this dictionary:
{'energy': 0.0005, # eV / electron
'density': 1.0e4, # electrons / electron
'eigenstates': 4.0e8, # eV^2 / electron
'bands': 'occupied'}
In words:
The energy change (last 3 iterations) should be less than 0.5 meV per valence electron. (See
Energy
.)The change in density (integrated absolute value of density change) should be less than 0.0001 electrons per valence electron. (See
Density
.)The integrated value of the square of the residuals of the KohnSham equations should be less than 4.0 \(\times\) 10^{8} eV^{2} per valence electron. This criterion does not affect LCAO calculations. (See
Eigenstates
.)Only the bands that are occupied with electrons are converged.
If only a partial dictionary is provided, the remaining criteria will be set
to their default values.
E.g., convergence={'energy': 0.0001}
will set the convergence criterion
of energy to 0.1 meV and place all other criteria at their defaults.
Additional keywords, including 'forces'
, 'work function'
,
and 'minimum iterations'
, can be set.
You can also write your own criteria, and change other things about
how the default criteria operate. See Custom convergence criteria for
details on additional keywords and customization.
As the total energy and charge density depend only on the occupied
states, unoccupied states do not contribute to the convergence
criteria. However, with the 'bands'
set to 'all'
, it is
possible to force convergence also for the unoccupied states. One can
also use {'bands': 200}
to converge the lowest 200 bands. One can
also write {'bands': 10}
to converge all bands except the last
10. It is often hard to converge the last few bands in a calculation.
Finally, one can also use {'bands': 'CBM+5.0'}
to specify that bands
up to the conduction band minimum plus 5.0 eV should be converged
(for a metal, CBM is taken as the Fermi level).
Maximum number of SCFiterations¶
The calculation will stop with an error if convergence is not reached in
maxiter
selfconsistent iterations. You can also set a minimum number of
iterations by employing a
custom convergence criterion.
Where to send text output¶
The txt
keyword defaults to the string ''
, which means
standard output. One can also give a file
object (anything with a
write
method will do). If a string (different from ''
) is
passed to the txt
keyword, a file with that name will be opened
and used for output. Use txt=None
to disable all text output.
Density mixing¶
Three parameters determine how GPAW does Pulay mixing of the densities:
beta
: linear mixing coefficientnmaxold
: number of old densities to mixweight
: when measuring the change from input to output density, long wavelength changes are weightedweight
times higher than short wavelength changes
For small molecules, the best choice is to use
mixer=Mixer(beta=0.25, nmaxold=3, weight=1.0)
, which is what GPAW
will choose if the system has zeroboundary conditions.
If your system is a big molecule or a cluster, it is an advantage to
use something like mixer=Mixer(beta=0.05, nmaxold=5, weight=50.0)
,
which is also what GPAW will choose if the system has periodic
boundary conditions in one or more directions.
In spinpolarized calculations MixerDif
will be used instead of
Mixer
.
See also the documentation on density mixing.
Fixed density calculation¶
When calculating band structures or when adding unoccupied states to
calculation (and wanting to converge them) it is often useful to use existing
density without updating it. This can be done using the
gpaw.calculator.GPAW.fixed_density()
method. This will use the density
(e.g. one read from .gpw or existing from previous calculation)
throughout the SCFcycles (so called Harris calculation).
PAW datasets or pseudopotentials¶
The setups
keyword is used to specify the name(s) of the setup files
used in the calculation.
For a given element E
, setup name NAME
, and xcfunctional
‘XC’, GPAW looks for the file E.NAME.XC
or E.NAME.XC.gz
(in that order) in the setup locations
(see Installation of PAW datasets).
Unless NAME='paw'
, in which case it will simply look for
E.XC
(or E.XC.gz
).
The setups
keyword can be either a single string, or a dictionary.
If specified as a string, the given name is used for all atoms. If specified as a dictionary, each keys can be either a chemical symbol or an atom number. The values state the individual setup names.
The special key 'default'
can be used to specify the default setup
name. Thus setups={'default': 'paw'}
is equivalent to setups='paw'
which is the GPAW default.
As an example, the latest PAW setup of Na includes also the 6 semicore p
states in the valence, in order to use nondefault setup with only the 1 s
electron in valence (Na.1.XC.gz
) one can specify setups={'Na':
'1'}
There exist three special names that, if used, do not specify a file name:
'ae'
is used for specifying allelectron mode for an atom. I.e. no PAW or pseudo potential is used.'sg15'
specifies the SG15 optimized normconserving Vanderbilt pseudopotentials for the PBE functional. These have to be installed separately. Use gpaw installdata sg15 {<dir>} to download and unpack the pseudopotentials into<dir>/sg15_oncv_upf_<version>
. As of now, the SG15 pseudopotentials should still be considered experimental in GPAW. You can plot a UPF pseudopotential by runninggpawupfplot <pseudopotential>
. Here,<pseudopotential>
can be either a direct path to a UPF file or the symbol or identifier to search for in the GPAW setup paths.'hgh'
is used to specify a normconserving HartwigsenGoedeckerHutter pseudopotential (no installation necessary). Some elements have better semicore pseudopotentials. To use those, specify'hgh.sc'
for the elements or atoms in question.'ghost'
is used to indicated a ghost atom in LCAO mode, see Ghost atoms and basis set superposition errors.
If a dictionary contains both chemical element specifications and atomic number specifications, the latter is dominant.
An example:
setups={'default': 'soft', 'Li': 'hard', 5: 'ghost', 'H': 'ae'}
Indicates that the files named ‘hard’ should be used for lithium atoms, an allelectron potential is used for hydrogen atoms, atom number 5 is a ghost atom (even if it is a Li or H atom), and for all other atoms the files named ‘soft’ will be used.
Atomic basis set¶
The basis
keyword can be used to specify the basis set which
should be used in LCAO mode. This also affects the LCAO
initialization in FD or PW mode, where initial wave functions are
constructed by solving the KohnSham equations in the LCAO basis.
If basis
is a string, basis='basisname'
, then GPAW will
look for files named symbol.basisname.basis
in the setup
locations (see Installation of PAW datasets), where
symbol
is taken as the chemical symbol from the Atoms
object. If a nondefault setup is used for an element, its name is
included as symbol.setupname.basisname.basis
.
If basis
is a dictionary, its keys specify atoms or species while
its values are corresponding basis names which work as above.
Distinct basis sets can be specified
for each atomic species by using the atomic symbol as
a key, or for individual atoms by using an int
as a key. In the
latter case the integer corresponds to the index of that atom in the
Atoms
object. As an example, basis={'H': 'sz', 'C': 'dz', 7:
'dzp'}
will use the sz
basis for hydrogen atoms, the dz
basis for carbon, and the dzp
for whichever atom is number 7 in
the Atoms
object.
Note
If you want to use only the sz
basis functinons from a dzp
basis set, then you can use this syntax: basis='sz(dzp)'
.
This will read the basis functions for, say hydrogen, from the
H.dzp.basis
file. If the basis has a custom name,
it is specified as 'szp(mybasis.dzp)'
.
The value None
(default) implies that the pseudo partial waves
from the setup are used as a basis. This basis is always available;
choosing anything else requires the existence of the corresponding
basis set file in setup locations
(see Installation of PAW datasets).
For details on the LCAO mode and generation of basis set files; see the LCAO documentation.
Eigensolver¶
The default solver for iterative diagonalization of the KohnSham
Hamiltonian is a simple Davidson method, (eigensolver='dav'
), which
seems to perform well in most cases. Sometimes more efficient/stable
convergence can be obtained with a different eigensolver. One option is the
RMMDIIS (Residual minimization method  direct inversion in iterative
subspace), (eigensolver='rmmdiis'
), which performs well when only a few
unoccupied states are calculated. Another option is the conjugate gradient
method (eigensolver='cg'
), which is stable but slower.
If parallellization over bands is necessary, then Davidson or RMMDIIS must be used.
More control can be obtained by using directly the eigensolver objects:
from gpaw.eigensolvers import CG
calc = GPAW(..., eigensolver=CG(niter=5, rtol=0.20), ...)
Here, niter
specifies the maximum number of conjugate gradient iterations
for each band (within a single SCF step), and if the relative change
in residual is less than rtol
, the iteration for the band is not continued.
LCAO mode has its own eigensolvers. DirectLCAO
eigensolver directly
diagonalizes the Hamiltonian matrix instead of using an iterative method.
One can also use Exponential Transformation Direct Minimization (ETDM) method
(see Direct Minimization Methods) but it is not recommended to use it for metals
because occupation numbers are not found variationally in ETDM.
Poisson solver¶
The poissonsolver
keyword is used to specify a Poisson solver class
or enable dipole correction.
The default Poisson solver in FD and LCAO mode
is called FastPoissonSolver
and uses
a combination of Fourier and Fouriersine transforms
in combination with parallel array transposes. Meanwhile in PW mode,
the Poisson equation is solved by dividing each planewave coefficient
by the squared length of its corresponding wavevector.
The old default Poisson solver uses a multigrid Jacobian method. This example corresponds to the old default Poisson solver:
from gpaw import GPAW, PoissonSolver
calc = GPAW(...,
poissonsolver=PoissonSolver(
name='fd', nn=3, relax='J', eps=2e10),
...)
The nn
argument is the stencil, see Finitedifference stencils.
The relax
argument is the method, either 'J'
(Jacobian) or 'GS'
(GaussSeidel). The GaussSeidel method requires half as many
iterations to solve the Poisson equation, but involves more
communication. The GaussSeidel implementation also depends slightly
on the domain decomposition used.
The last argument, eps
, is the convergence criterion.
Note
The Poisson solver is rarely a performance bottleneck, but it can sometimes perform poorly depending on the grid layout. This is mostly important in LCAO calculations, but can be good to know in general. See the LCAO notes on Poisson performance.
The poissonsolver
keyword can also be used to specify that a dipole
correction should be applied along a given axis. The system should be
nonperiodic in that direction but periodic in the two other
directions.
from gpaw import GPAW
correction = {'dipolelayer': 'xy'}
calc = GPAW(..., poissonsolver=correction, ...)
Without dipole correction, the potential will approach 0 at all nonperiodic boundaries. With dipole correction, there will be a potential difference across the system depending on the size of the dipole moment.
Other parameters in this dictionary are forwarded to the Poisson solver:
GPAW(...,
poissonsolver={'dipolelayer': 'xy', 'name': 'fd', 'relax': 'GS'},
...)
An alternative Poisson solver based on Fourier transforms is available for fully periodic calculations:
GPAW(..., poissonsolver={'name': 'fft'}, ...)
The FFT Poisson solver will reduce the dependence on the grid spacing and is in general less picky about the grid. It may be beneficial for nonperiodic systems as well, but the system must be set up explicitly as periodic and hence should be well padded with vacuum in nonperiodic directions to avoid unphysical interactions across the cell boundary.
Finitedifference stencils¶
GPAW can use finitedifference stencils for the Laplacian in the KohnSham and Poisson equations. You can set the range of the stencil (number of neighbor grid points) used for the Poisson equation like this:
from gpaw import GPAW, PoissonSolver
calc = GPAW(..., poissonsolver=PoissonSolver(nn=n), ...)
This will give an accuracy of \(O(h^{2n})\), where n
must be between
1 and 6. The default value is n=3
.
Similarly, for the KohnSham equation, you can use:
from gpaw import GPAW, FD
calc = GPAW(mode=FD(nn=n))
where the default value is also n=3
.
In PWmode, the interpolation of the density from the coarse grid to the fine grid is done with FFT’s. In FD and LCAO mode, triquintic interpolation is used (5. degree polynomium):
from gpaw import GPAW, FD
calc = GPAW(mode=FD(interpolation=n))
# or
from gpaw import GPAW, LCAO
calc = GPAW(mode=LCAO(interpolation=n))
The order of polynomium is \(2n1\), default value is n=3
and n
must be
between 1 and 4 (linear, cubic, quintic, heptic).
Using Hund’s rule for guessing initial magnetic moments¶
With hund=True
, the calculation will become spinpolarized, and the initial
ocupations, and magnetic moments of all atoms will be set to the values
required by Hund’s rule. You may further wish to specify that the
total magnetic moment be fixed, by passing e.g. occupations={'name': ...,
'fixmagmom': True}
. Any user specified magnetic moment is ignored. Default
is False.
External potential¶
Example:
from gpaw.external import ConstantElectricField
calc = GPAW(..., external=ConstantElectricField(2.0, [1, 0, 0]), ...)
See also: gpaw.external
.
Output verbosity¶
By default, only a limited number of information is printed out for each SCF
step. It is possible to obtain more information (e.g. for investigating
convergen problems in more detail) by verbose=1
keyword.
Communicator object¶
By specifying a communicator object, it is possible to use only a subset of processes for the calculator when calculating e.g. different atomic images in parallel. See Running different calculations in parallel for more details.
Total Energies¶
The GPAW code calculates energies relative to the energy of separated reference atoms, where each atom is in a spinpaired, neutral, and spherically symmetric state  the state that was used to generate the setup. For a calculation of a molecule, the energy will be minus the atomization energy and for a solid, the resulting energy is minus the cohesive energy. So, if you ever get positive energies from your calculations, your system is in an unstable state!
Note
You don’t get the true atomization/cohesive energy. The true number is always lower, because most atoms have a spinpolarized and nonspherical symmetric ground state, with an energy that is lower than that of the spinpaired, and spherically symmetric reference atom.
Restarting a calculation¶
The state of a calculation can be saved to a file like this:
>>> calc.write('H2.gpw')
The file H2.gpw
is a binary file containing
wave functions, densities, positions and everything else (also the
parameters characterizing the PAW calculator used for the
calculation).
If you want to restart the \(\rm{H}_2\) calculation in another Python session at a later time, this can be done as follows:
>>> from gpaw import *
>>> atoms, calc = restart('H2.gpw')
>>> print(atoms.get_potential_energy())
Everything will be just as before we wrote the H2.gpw
file.
Often, one wants to restart the calculation with one or two parameters
changed slightly. This is simple to do. Suppose you want to
change the number of grid points:
>>> atoms, calc = restart('H2.gpw', gpts=(20, 20, 20))
>>> print(atoms.get_potential_energy())
Tip
There is an alternative way to do this, that can be handy sometimes:
>>> atoms, calc = restart('H2.gpw')
>>> calc.set(gpts=(20, 20, 20))
>>> print(atoms.get_potential_energy())
More details can be found on the Restart files page.
Customizing behaviour through observers¶
An observer function can be attached to the calculator so that it will be executed every N iterations during a calculation. The below example saves a differently named restart file every 5 iterations:
calc = GPAW(...)
occasionally = 5
class OccasionalWriter:
def __init__(self):
self.iter = 0
def write(self):
calc.write('filename.%03d.gpw' % self.iter)
self.iter += occasionally
calc.attach(OccasionalWriter().write, occasionally)
See also attach()
.
Commandline options¶
I order to run GPAW in debugmode, e.g. check consistency of arrays passed
to Cextensions, use Python’s d
option:
$ python3 d script.py
If you run Python through the gpaw python
command, then you can run your
script in dryrun mode like this:
$ gpaw python dryrun=N script.py
This will print out the computational parameters and estimate
memory usage, and not perform an actual calculation.
Parallelization settings that would be employed when run on
N
cores will also be printed.
Tip
If you need extra parameters from the commandline for development work:
$ python3 X a=1 X b
>>> import sys
>>> sys._xoptions
{'a': '1', 'b': True}
See also Python’s X
option.