# 2.4. Beyond transport: Band structure and closed systems¶

## 2.4.1. Band structure calculations¶

The complete source code of this example can be found in `tutorial/band_structure.py`

When doing transport simulations, one also often needs to know the band structure of the leads, i.e. the energies of the propagating plane waves in the leads as a function of momentum. This band structure contains information about the number of modes, their momenta and velocities.

In this example, we aim to compute the band structure of a simple tight-binding wire.

Computing band structures in Kwant is easy. Just define a lead in the usual way:

```def make_lead(a=1, t=1.0, W=10):
lat = kwant.lattice.square(a)

# build up one unit cell of the lead, and add the hoppings
# to the next unit cell
for j in xrange(W):
lead[lat(0, j)] = 4 * t

if j > 0:
lead[lat(0, j), lat(0, j - 1)] = -t

lead[lat(1, j), lat(0, j)] = -t

```

“Usual way” means defining a translational symmetry vector, as well as one unit cell of the lead, and the hoppings to neighboring unit cells. This information is enough to make the infinite, translationally invariant system needed for band structure calculations.

In the previous examples `Builder` instances like the one created above were attached as leads to the `Builder` instance of the scattering region and the latter was finalized. The thus created system contained implicitly finalized versions of the attached leads. However, now we are working with a single lead and there is no scattering region. Hence, we have to finalize the `Builder` of our sole lead explicitly.

That finalized lead is then passed to `bands`. This function calculates energies of various bands at a range of momenta and plots the calculated energies. It is really a convenience function, and if one needs to do something more profound with the dispersion relation these energies may be calculated directly using `Bands`. For now we just plot the bandstructure:

```def main():
pyplot.xlabel("momentum [(lattice constant)^-1]")
pyplot.ylabel("energy [t]")
pyplot.show()
```

This gives the result:

where we observe the cosine-like dispersion of the square lattice. Close to `k=0` this agrees well with the quadratic dispersion this tight-binding Hamiltonian is approximating.

## 2.4.2. Closed systems¶

The complete source code of this example can be found in `tutorial/closed_system.py`

Although Kwant is (currently) mainly aimed towards transport problems, it can also easily be used to compute properties of closed systems – after all, a closed system is nothing more than a scattering region without leads!

In this example, we compute the wave functions of a closed circular quantum dot and its spectrum as a function of magnetic field (Fock-Darwin spectrum).

To compute the eigenenergies and eigenstates, we will make use of the sparse linear algebra functionality of SciPy, which interfaces the ARPACK package:

```import scipy.sparse.linalg as sla
```

We set up the system using the shape-function as in Nontrivial shapes, but do not add any leads:

```    lat = kwant.lattice.square(a)

sys = kwant.Builder()

# Define the quantum dot
def circle(pos):
(x, y) = pos
rsq = x ** 2 + y ** 2
return rsq < r ** 2

def hopx(site1, site2, B=0):
# The magnetic field is controlled by the parameter B
y = site1.pos[1]
return -t * exp(-1j * B * y)

sys[lat.shape(circle, (0, 0))] = 4 * t
# hoppings in x-direction
sys[kwant.builder.HoppingKind((1, 0), lat, lat)] = hopx
# hoppings in y-directions
sys[kwant.builder.HoppingKind((0, 1), lat, lat)] = -t

# It's a closed system for a change, so no leads
return sys
```

We add the magnetic field using a function and a global variable as we did in the two previous tutorial. (Here, the gauge is chosen such that and .)

The spectrum can be obtained by diagonalizing the Hamiltonian of the system, which in turn can be obtained from the finalized system using `hamiltonian_submatrix`:

```def plot_spectrum(sys, Bfields):

# In the following, we compute the spectrum of the quantum dot
# using dense matrix methods. This works in this toy example, as
# the system is tiny. In a real example, one would want to use
# sparse matrix methods

energies = []
for B in Bfields:
# Obtain the Hamiltonian as a dense matrix
ham_mat = sys.hamiltonian_submatrix(args=[B], sparse=True)

# we only calculate the 15 lowest eigenvalues
ev = sla.eigsh(ham_mat, k=15, which='SM', return_eigenvectors=False)

energies.append(ev)

pyplot.figure()
pyplot.plot(Bfields, energies)
pyplot.xlabel("magnetic field [arbitrary units]")
pyplot.ylabel("energy [t]")
pyplot.show()
```

Note that we use sparse linear algebra to efficiently calculate only a few lowest eigenvalues. Finally, we obtain the result:

At zero magnetic field several energy levels are degenerate (since our quantum dot is rather symmetric). These degeneracies are split by the magnetic field, and the eigenenergies flow towards the Landau level energies at higher magnetic fields [1].

The eigenvectors are obtained very similarly, and can be plotted directly using `map`:

```def plot_wave_function(sys):
# Calculate the wave functions in the system.
ham_mat = sys.hamiltonian_submatrix(sparse=True)
evecs = sla.eigsh(ham_mat, k=20, which='SM')[1]

# Plot the probability density of the 10th eigenmode.
kwant.plotter.map(sys, np.abs(evecs[:, 9])**2,
colorbar=False, oversampling=1)
```

The last two arguments to `map` are optional. The first prevents a colorbar from appearing. The second, `oversampling=1`, makes the image look better for the special case of a square lattice.

Technical details
• `hamiltonian_submatrix` can also return a sparse matrix, if the optional argument `sparse=True`. The sparse matrix is in SciPy’s `scipy.sparse.coo_matrix` format, which can be easily be converted to various other sparse matrix formats (see SciPy’s documentation).

Footnotes

 [1] Again, in this tutorial example no care was taken into choosing appropriate material parameters or units. For this reason, magnetic field is given only in “arbitrary units”.