Basics of GPAW calculations

Atomization energy revisited

The below script calculates the total energies of H2O, H and O.

from __future__ import print_function
from import molecule
from gpaw import GPAW

a = 8.0
h = 0.2

energies = {}
resultfile = open('results-%.2f.txt' % h, 'w')

for name in ['H2O', 'H', 'O']:
    system = molecule(name)
    system.set_cell((a, a, a))

    calc = GPAW(h=h,
                txt='gpaw-%s-%.2f.txt' % (name, h))
    if name == 'H' or name == 'O':


    energy = system.get_potential_energy()
    energies[name] = energy
    print(name, energy, file=resultfile)

e_atomization = energies['H2O'] - 2 * energies['H'] - energies['O']
print(e_atomization, file=resultfile)

Read the script and try to understand what it does. A few notes:

  • In most languages it is common to declare loops with integer counters. In Python, a for loop always loops over elements of an iterable (in this case a list of the strings H2O, H and O). You can loop over integers, if desired, using for x in range(17):.
  • The code in loops, if-statements and other code blocks is indented. The loop or if-statement stops when the lines are no longer indented. Thus, indentation determines control flow.
  • In this case we conveniently load the geometry from the G2 database of small molecules, using the molecule() function from ASE.
  • By setting the txt parameter, we specify a file where GPAW will save the calculation log.
  • The expression 'results-%.2f.txt' % h inserts the value of h in place of the substitution code %.2f (floating point number with 2 decimals). Thus the result file name evaluates to results-0.20.txt. Similarly, 'gpaw-%s-%.2f.txt' % (name, h) evaluates to gpaw-H2O-0.20.txt in the first loop iteration (%s is a substitution code for a string).
  • The call to open opens a file. The parameter 'w' signifies that the file is opened in write mode (deleting any previous file with that name!). The calculated energies are written to this file using a print statement. We could have chosen to just print the parameter as in the last exercise, but file handling will come in handy below.

Run the script. You can monitor the progress by opening one of the log files (e.g. gpaw.H2O.txt). The command tail -f filename can be used to view the output in real-time. The calculated atomization energy can be found in the results-0.20.txt file.


To speed things up, let us run the script using some more CPUs. The actual GPAW calculation is coded to make proper use of these extra CPUs, while the rest of the script (everything except calc.get_potential_energy()) is actually going to be run independently by each CPU. This matters little except when writing to files. Each CPU will attempt to write to the same file, probably at the same time, producing garbled data. We must therefore make sure that only one process writes. ASE provides the handy paropen() function for just that:

from ase.parallel import paropen
resultfile = paropen('results-%.2f.txt' % h, 'w')

Apply the above modifications to the script and run it in parallel e.g. on four CPUs:

$ mpirun -np 4 gpaw-python

Verify by checking the log file that GPAW is actually using multiple CPUs. The log file should reveal that the H2O calculation uses domain-decomposed with four domains, while the two atomic calculations should parallelize over the two spins and two domains.

Convergence checks

It is essential that the calculations use a sufficiently fine grid spacing, and that the cell is sufficiently large not to affect the result. For this reason, convergence with respect to these parameters should generally be checked. For now we shall only bother to check the grid spacings.

Modify the above script to include a loop over different grid spacings. For technical reasons, GPAW will always use a number of grid points divisible by four along each direction. Use a loop structure like:

for symbol in [...]:

    for ngridpoints in [24, 28, ...]:
        h = a / ngridpoints
        energy = system.get_potential_energy()

The set method can be used to change the parameters of a calculator without creating a new one. Make sure that the numbers of grid points are chosen to cover \(0.15<h<0.25\). While performing this convergence check, the other parameters do not need to be converged - you can reduce the cell size to e.g. a = 6.0 to improve performance. You may wish to run the calculation in parallel.

  • How do the total energies converge with respect to grid spacing?
  • How does the atomization energy converge?

Total energies (maybe surprisingly) do not drop as the grid is refined. This would be the case in plane-wave methods, where increase of the planewave cutoff strictly increases the quality of the basis. Grid-based methods rely on finite-difference stencils, where the gradient in one point is calculated from the surrounding points. This makes the grid strictly inequivalent to a basis, and thus not (necessarily) variational.

LCAO calculations

GPAW supports an alternative calculation mode, LCAO mode, which uses linear combinations of pre-calculated atomic orbitals to represent the wavefunctions. LCAO calculations are much faster for most systems, but also less precise.

Performing an LCAO calculation requires setting the mode and normally a basis:

calc = GPAW(...,

Here dzp (“double-zeta polarized”) means two basis functions per valence state plus a polarization function - a function corresponding to the lowest unoccupied angular-momentum state on the atom. This will use the standard basis sets distributed with GPAW. You can pick out a smaller basis set using the special syntax basis='sz(dzp)'. This will pick out only one function per valence state (“single-zeta”), making the calculation even faster but less precise.

Calculate the atomization energy of H2O using different basis sets. Instead of looping over grid spacing, use a loop over basis keywords:

for basis in ['sz(dzp)', 'szp(dzp)', 'dzp']:
    calc = GPAW(mode='lcao',

Compare the calculated energies to those calculated in grid mode. Do the energies deviate a lot? What about the atomization energy? Is the energy variational with respect to the quality of the basis?

LCAO calculations do not in fact produce very precise binding energies (although these can be improved considerably by manually generating optimized basis functions) - however the method is well suited to calculate geometries, and for applications that require a small basis set, such as electron transport calculations.

Plane-wave calculations

For systems with small unit-cells, it can be much faster to expand the wave-functions in plane-waves. Try running a calculation for a water molecule with a plane-wave cutoff of 350 eV using this:

from gpaw import GPAW, PW
calc = GPAW(mode=PW(350), ...)

Try to look at the text output and see if you can find the number of plane-waves used.