The PoissonSolver with default parameters uses zero boundary conditions on the cell boundaries. This becomes a problem in systems involving large dipole moment, for example (due to, e.g., plasmonic charge oscillation on a nanoparticle). The potential due to the dipole is long-ranged and, thus, the converged potential requires large vacuum sizes.

However, in LCAO approach large vacuum size is often unnecessary. Thus, to avoid using large vacuum sizes but get converged potential, one can use two approaches or their combination: 1) use multipole moment corrections or 2) solve Poisson equation on a extended grid. These two approaches are implemented in MomentCorrectionPoissonSolver and ExtraVacuumPoissonSolver.

In any nano-particle plasmonics calculation, it is necessary to use multipole correction. Without corrections more than 10Å of vacuum is required for converged results.

## Multipole moment corrections¶

The boundary conditions can be improved by adding multipole moment corrections to the density so that the corresponding multipoles of the density vanish. The potential of these corrections is added to the obtained potential. For a description of the method, see [1].

This can be accomplished by following solver:

from gpaw.poisson import PoissonSolver
from gpaw.poisson_moment import MomentCorrectionPoissonSolver
poissonsolver = MomentCorrectionPoissonSolver(poissonsolver=PoissonSolver(),
moment_corrections=4)


This corrects the 4 first multipole moments, i.e., $$s$$, $$p_x$$, $$p_y$$, and $$p_z$$ type multipoles. The potential of the corrected density is solved with the given poissonsolver. The range of multipoles can be changed by changing moment_corrections parameter. For example, moment_correction=9 includes in addition to the previous multipoles, also $$d_{xx}$$, $$d_{xy}$$, $$d_{yy}$$, $$d_{yz}$$, and $$d_{zz}$$ type multipoles.

This setting suffices usually for spherical-like metallic nanoparticles, but more complex geometries require inclusion of very high multipoles or, alternatively, a multicenter multipole approach. For this, consider the advanced syntax of the moment_corrections. The previous code snippet is equivalent to:

from gpaw.poisson import PoissonSolver
from gpaw.poisson_moment import MomentCorrectionPoissonSolver
poissonsolver = MomentCorrectionPoissonSolver(poissonsolver=PoissonSolver(),
moment_corrections=[{'moms': range(4), 'center': None}])


Here moment_corrections is a list of dictionaries with following keywords: moms specifies the considered multipole moments, e.g., range(4) equals to $$s$$, $$p_x$$, $$p_y$$, and $$p_z$$ multipoles, and center specifies the center of the added corrections in atomic units (None corresponds to the center of the cell).

As an example, consider metallic nanoparticle dimer where the nanoparticle centers are at (x1, y1, z1) Å and (x2, y2, z2) Å. In this case, the following settings for the MomentCorrectionPoissonSolver may be tried out:

import numpy as np
from gpaw.poisson import PoissonSolver
from gpaw.poisson_moment import MomentCorrectionPoissonSolver
moms = range(4)
center1 = np.array([x1, y1, z1])
center2 = np.array([x2, y2, z2])
poissonsolver = MomentCorrectionPoissonSolver(poissonsolver=PoissonSolver(),
moment_corrections=[{'moms': moms, 'center': center1},
{'moms': moms, 'center': center2}])


When multiple centers are used, the multipole moments are calculated on non-overlapping regions of the calculation cell. Each point in space is associated to its closest center. See Voronoi diagrams for analogous illustration of the partitioning of a plane.

## Adding extra vacuum to the Poisson grid¶

The multipole correction scheme is not always successful for complex system geometries. For these cases, one can use a separate large grid just for solving the Hartree potential. Such a large grid can be set up by using ExtraVacuumPoissonSolver wrapper:

from gpaw.poisson import PoissonSolver
from gpaw.poisson_extravacuum import ExtraVacuumPoissonSolver
poissonsolver = ExtraVacuumPoissonSolver(gpts=(256, 256, 256),
poissonsolver_large=PoissonSolver())


This uses the given poissonsolver_large to solve the Poisson equation on a large grid defined by the number of grid points $$gpts$$. The size of the grid is given in the units of the Poisson grid (this is usually the same as the fine grid). If using the FDPoissonSolver, it is important to use grid sizes that are divisible by high powers of 2 to accelerate the multigrid scheme.

To speed up the calculation of the Hartree potential on the large grid, one can apply additional coarsening:

from gpaw.poisson import PoissonSolver
from gpaw.poisson_extravacuum import ExtraVacuumPoissonSolver
poissonsolver = ExtraVacuumPoissonSolver(gpts=(256, 256, 256),
poissonsolver_large=PoissonSolver(),
coarses=1,
poissonsolver_small=PoissonSolver())


The coarses parameter describes how many times the given large grid is coarsed before the poissonsolver_large is used solve the Poisson equation there. With the given value coarses=1, the grid is coarsed once and the actual calculation grid is of size (128, 128, 128) with the grid spacing twice as large compared to the original one. The obtained coarse potential is used to correct the boundary conditions of the potential calculated on the original small and fine grid by poissonsolver_small.

As ExtraVacuumPoissonSolver is wrapper, it can be combined with any PoissonSolver instance. For example, one can define multiple subsequently larger grids via:

from gpaw.poisson import PoissonSolver
from gpaw.poisson_extravacuum import ExtraVacuumPoissonSolver
poissonsolver0 = ExtraVacuumPoissonSolver(gpts=(256, 256, 256),
poissonsolver_large=PoissonSolver(),
coarses=1,
poissonsolver_small=PoissonSolver())
poissonsolver = ExtraVacuumPoissonSolver(gpts=(256, 256, 256),
poissonsolver_large=poissonsolver0,
coarses=1,
poissonsolver_small=PoissonSolver())


See poissonsolver.get_description() or the txt output for the corresponding grids.