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Core NanoLanguage elements |  |
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| calculateAtomicForces | calculateElectronDensity |
Function that calculates and returns the local real-space
self-consistent Kohn-Sham effective potential 
.
List of arguments
An object returned from a previously performed self-consistent calculation.
Default:
None
The returned object from calculateEffectivePotential() has the following two query methods:
Array toArray(): Returns the
real-space representation of the effective potential in a NumPy array with
dimensions
(n1,n2,n3)
for a spin-unpolarized calculation and
(2,n1,n2,n3)
for a spin-polarized calculation with the spin-up component as the first entry.
PhysicalUnit toUnit():
Returns the unit of the effective potential.
from ATK.KohnSham import * configuration = ... scf = executeSelfConsistentCalculation(...) effective_potential = calculateEffectivePotential(scf) f = VNLFile("results.vnl") f.addToSample(configuration,"My sample") f.addToSample(effective_potential,"My sample")
As shown in the above example, the returned object from this function can be placed in a VNL file; this can then be visualized in the Nanoscope in Virtual NanoLab.
The dimension of the returned array is 
for
spin-polarized calculations and 
otherwise. The size of
the array, i.e. the numbers 
depends on the mesh cut-off. The
shape of the array can be obtained using the shape
property, if you for example would like to perform calculations using the raw
data:
effective_potential_object = calculateEffectivePotential(scf) effective_potential = effective_potential_object.toArray() unit = effective_potential_object.toUnit() array_shape = effective_potential.shape print array_shape (2,100,124,226) # example output
The output example refers to a spin-polarized calculation, where the leading
dimension is 2. Note that shape is an object property
(i.e. there are no parentheses), and not a method like
toUnit()!
The object returned from this function supports basic algebraic operations such as addition and subtraction. This, for example, makes it possible to calculate the voltage drop across a two-probe system, by making two calculations at zero and finite bias, respectively, and subtracting the effective potentials. The result of the operation is still of the type LocalPotential and can be added to a VNL file:
from ATK.TwoProbe import * atomic_configuration = ... # zero bias calculation scf_0V = executeSelfConsistentCalculation(...) # bias calculation scf_1V = executeSelfConsistentCalculation(...) effpot_0V = calculateEffectivePotential(scf_0V) effpot_1V = calculateEffectivePotential(scf_1V) voltage_drop = pot_1V - pot_0V vnl_file = VNLFile("voltage_drop.vnl") vnl_file.addToSample(atomic_configuration,'my sample') vnl_file.addToSample(voltage_drop,'my sample')
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| calculateAtomicForces |
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calculateElectronDensity |