# 2.10. Discretizing continuous Hamiltonians#

## Introduction#

In “Discretization of a Schrödinger Hamiltonian” we have learnt that Kwant works
with tight-binding Hamiltonians. Often, however, one will start with a
continuum model and will subsequently need to discretize it to arrive at a
tight-binding model.
Although discretizing a Hamiltonian is usually a simple
process, it is tedious and repetitive. The situation is further exacerbated
when one introduces additional on-site degrees of freedom, and tracking all
the necessary terms becomes a chore.
The `continuum`

sub-package aims to be a solution to this problem.
It is a collection of tools for working with
continuum models and for discretizing them into tight-binding models.

See also

You can execute the code examples live in your browser by activating thebelab:

See also

The complete source code of this tutorial can be found in
`discretize.py`

```
# Tutorial 2.9. Processing continuum Hamiltonians with discretize
# ===============================================================
#
# Physics background
# ------------------
# - tight-binding approximation of continuous Hamiltonians
#
# Kwant features highlighted
# --------------------------
# - kwant.continuum.discretize
import matplotlib as mpl
from matplotlib import pyplot
import kwant
```

```
import matplotlib
import matplotlib.pyplot
from matplotlib_inline.backend_inline import set_matplotlib_formats
matplotlib.rcParams['figure.figsize'] = matplotlib.pyplot.figaspect(1) * 2
set_matplotlib_formats('svg')
```

## Discretizing by hand#

As an example, let us consider the following continuum Schrödinger equation for a semiconducting heterostructure (using the effective mass approximation):

Replacing the momenta by their corresponding differential operators

for \(\alpha = x, y\) or \(z\), and discretizing on a regular lattice of points with spacing \(a\), we obtain the tight-binding model

with \(A(x) = \frac{\hbar^2}{2 m(x)}\).

## Using `discretize`

to obtain a template#

First we must explicitly import the `kwant.continuum`

package:

```
import kwant.continuum
```

```
import scipy.sparse.linalg
import scipy.linalg
import numpy as np
```

The function `kwant.continuum.discretize`

takes a symbolic Hamiltonian and
turns it into a `Builder`

instance with appropriate spatial
symmetry that serves as a template.
(We will see how to use the template to build systems with a particular
shape later).

```
template = kwant.continuum.discretize('k_x * A(x) * k_x')
print(template)
```

```
# Discrete coordinates: x
# Onsite element:
def onsite(site, A):
(x, ) = site.pos
_const_0 = (A(0.5 + x))
_const_1 = (A(-0.5 + x))
return (1.0*_const_0 + 1.0*_const_1)
# Hopping from (1,):
def hopping_1(site1, site2, A):
(x, ) = site1.pos
_const_0 = (A(0.5 + x))
return (-1.0*_const_0)
```

It is worth noting that `discretize`

treats `k_x`

and `x`

as
non-commuting operators, and so their order is preserved during the
discretization process.

Printing the Builder produced by `discretize`

shows the source code of its onsite and hopping functions (this is a special feature of builders returned by `discretize`

).

Technical details

`kwant.continuum`

uses`sympy`

internally to handle symbolic expressions. Strings are converted using`kwant.continuum.sympify`

, which essentially applies some Kwant-specific rules (such as treating`k_x`

and`x`

as non-commutative) before calling`sympy.sympify`

The builder returned by

`discretize`

will have an N-D translational symmetry, where`N`

is the number of dimensions that were discretized. This is the case, even if there are expressions in the input (e.g.`V(x, y)`

) which in principle*may not*have this symmetry. When using the returned builder directly, or when using it as a template to construct systems with different/lower symmetry, it is important to ensure that any functional parameters passed to the system respect the symmetry of the system. Kwant provides no consistency check for this.The discretization process consists of taking input \(H(k_x, k_y, k_z)\), multiplying it from the right by \(\psi(x, y, z)\) and iteratively applying a second-order accurate central derivative approximation for every \(k_\alpha=-i\partial_\alpha\):

\[\partial_\alpha \psi(\alpha) = \frac{1}{a} \left( \psi\left(\alpha + \frac{a}{2}\right) -\psi\left(\alpha - \frac{a}{2}\right)\right).\]This process is done separately for every summand in Hamiltonian. Once all symbols denoting operators are applied internal algorithm is calculating

`gcd`

for hoppings coming from each summand in order to find best possible approximation. Please see source code for details.Instead of using

`discretize`

one can use`discretize_symbolic`

to obtain symbolic output. When working interactively in Jupyter notebooks it can be useful to use this to see a symbolic representation of the discretized Hamiltonian. This works best when combined with`sympy`

Pretty Printing.The symbolic result of discretization obtained with

`discretize_symbolic`

can be converted into a builder using`build_discretized`

. This can be useful if one wants to alter the tight-binding Hamiltonian before building the system.

## Building a Kwant system from the template#

Let us now use the output of `discretize`

as a template to
build a system and plot some of its energy eigenstate. For this example the
Hamiltonian will be

where \(V(x, y)\) is some arbitrary potential.

First, use `discretize`

to obtain a
builder that we will use as a template:

```
hamiltonian = "k_x**2 + k_y**2 + V(x, y)"
template = kwant.continuum.discretize(hamiltonian)
print(template)
```

```
# Discrete coordinates: x y
# Onsite element:
def onsite(site, V):
(x, y, ) = site.pos
_const_0 = (V(x, y))
return (_const_0 + 4.0)
# Hopping from (1, 0):
(-1+0j)
# Hopping from (0, 1):
(-1+0j)
```

We now use this system with the `fill`

method of `Builder`

to construct the system we
want to investigate:

```
def stadium(site):
(x, y) = site.pos
x = max(abs(x) - 20, 0)
return x**2 + y**2 < 30**2
syst = kwant.Builder()
syst.fill(template, stadium, (0, 0));
syst = syst.finalized()
```

After finalizing this system, we can plot one of the system’s energy eigenstates:

```
def plot_eigenstate(syst, n=2, Vx=.0003, Vy=.0005):
def potential(x, y):
return Vx * x + Vy * y
ham = syst.hamiltonian_submatrix(params=dict(V=potential), sparse=True)
evecs = scipy.sparse.linalg.eigsh(ham, k=10, which='SM')[1]
kwant.plotter.map(syst, abs(evecs[:, n])**2, show=False)
```

```
plot_eigenstate(syst)
```

Note in the above that we pass the spatially varying potential *function*
to our system via a parameter called `V`

, because the symbol \(V\)
was used in the initial, symbolic, definition of the Hamiltonian.

In addition, the function passed as `V`

expects two input parameters `x`

and `y`

, the same as in the initial continuum Hamiltonian.

## Models with more structure: Bernevig-Hughes-Zhang#

When working with multi-band systems, like the Bernevig-Hughes-Zhang (BHZ)
model [1] [2], one can provide matrix input to `discretize`

using `identity`

and `kron`

. For example, the definition of the BHZ model can be
written succinctly as:

```
hamiltonian = """
+ C * identity(4) + M * kron(sigma_0, sigma_z)
- B * (k_x**2 + k_y**2) * kron(sigma_0, sigma_z)
- D * (k_x**2 + k_y**2) * kron(sigma_0, sigma_0)
+ A * k_x * kron(sigma_z, sigma_x)
- A * k_y * kron(sigma_0, sigma_y)
"""
a = 20
template = kwant.continuum.discretize(hamiltonian, grid=a)
```

We can then make a ribbon out of this template system:

```
L, W = 2000, 1000
def shape(site):
(x, y) = site.pos
return (0 <= y < W and 0 <= x < L)
def lead_shape(site):
(x, y) = site.pos
return (0 <= y < W)
syst = kwant.Builder()
syst.fill(template, shape, (0, 0))
lead = kwant.Builder(kwant.TranslationalSymmetry([-a, 0]))
lead.fill(template, lead_shape, (0, 0))
syst.attach_lead(lead)
syst.attach_lead(lead.reversed())
syst = syst.finalized()
```

and plot its dispersion using `kwant.plotter.bands`

:

```
params = dict(A=3.65, B=-68.6, D=-51.1, M=-0.01, C=0)
kwant.plotter.bands(syst.leads[0], params=params,
momenta=np.linspace(-0.3, 0.3, 201), show=False)
pyplot.grid()
pyplot.xlim(-.3, 0.3)
pyplot.ylim(-0.05, 0.05)
pyplot.xlabel('momentum [1/A]')
pyplot.ylabel('energy [eV]')
pyplot.show()
```

In the above we see the edge states of the quantum spin Hall effect, which
we can visualize using `kwant.plotter.map`

:

```
# get scattering wave functions at E=0
wf = kwant.wave_function(syst, energy=0, params=params)
# prepare density operators
sigma_z = np.array([[1, 0], [0, -1]])
prob_density = kwant.operator.Density(syst, np.eye(4))
spin_density = kwant.operator.Density(syst, np.kron(sigma_z, np.eye(2)))
# calculate expectation values and plot them
wf_sqr = sum(prob_density(psi) for psi in wf(0)) # states from left lead
rho_sz = sum(spin_density(psi) for psi in wf(0)) # states from left lead
fig, (ax1, ax2) = pyplot.subplots(1, 2, sharey=True, figsize=(16, 4))
kwant.plotter.map(syst, wf_sqr, ax=ax1)
kwant.plotter.map(syst, rho_sz, ax=ax2)
ax = ax1
im = [obj for obj in ax.get_children()
if isinstance(obj, mpl.image.AxesImage)][0]
fig.colorbar(im, ax=ax)
ax = ax2
im = [obj for obj in ax.get_children()
if isinstance(obj, mpl.image.AxesImage)][0]
fig.colorbar(im, ax=ax)
ax1.set_title('Probability density')
ax2.set_title('Spin density')
pyplot.show()
```

## Limitations of discretization#

It is important to remember that the discretization of a continuum
model is an *approximation* that is only valid in the low-energy
limit. For example, the quadratic continuum Hamiltonian

and its discretized approximation

where \(t=\frac{\hbar^2}{2ma^2}\), are only valid in the limit \(E < t\). The grid spacing \(a\) must be chosen according to how high in energy you need your tight-binding model to be valid.

It is possible to set \(a\) through the `grid`

parameter
to `discretize`

, as we will illustrate in the following
example. Let us start from the continuum Hamiltonian

We start by defining this model as a string and setting the value of the \(α\) parameter:

```
hamiltonian = "k_x**2 * identity(2) + alpha * k_x * sigma_y"
params = dict(alpha=.5)
```

Now we can use `kwant.continuum.lambdify`

to obtain a function that computes
\(H(k)\):

```
h_k = kwant.continuum.lambdify(hamiltonian, locals=params)
k_cont = np.linspace(-4, 4, 201)
e_cont = [scipy.linalg.eigvalsh(h_k(k_x=ki)) for ki in k_cont]
```

We can also construct a discretized approximation using
`kwant.continuum.discretize`

, in a similar manner to previous examples:

```
def plot(ax, a=1):
template = kwant.continuum.discretize(hamiltonian, grid=a)
syst = kwant.wraparound.wraparound(template).finalized()
def h_k(k_x):
p = dict(k_x=k_x, **params)
return syst.hamiltonian_submatrix(params=p)
k_tb = np.linspace(-np.pi/a, np.pi/a, 201)
e_tb = [scipy.linalg.eigvalsh(h_k(k_x=a*ki)) for ki in k_tb]
ax.plot(k_cont, e_cont, 'r-')
ax.plot(k_tb, e_tb, 'k-')
ax.plot([], [], 'r-', label='continuum')
ax.plot([], [], 'k-', label='tight-binding')
ax.set_xlim(-4, 4)
ax.set_ylim(-1, 14)
ax.set_title('a={}'.format(a))
ax.set_xlabel('momentum [a.u.]')
ax.set_ylabel('energy [a.u.]')
ax.grid()
ax.legend()
```

Below we can see the continuum and tight-binding dispersions for two different values of the discretization grid spacing \(a\):

```
_, (ax1, ax2) = pyplot.subplots(1, 2, sharey=True, figsize=(12, 4))
plot(ax1, a=1)
plot(ax2, a=.25)
pyplot.show()
```

We clearly see that the smaller grid spacing is, the better we approximate the original continuous dispersion. It is also worth remembering that the Brillouin zone also scales with grid spacing: \([-\frac{\pi}{a}, \frac{\pi}{a}]\).

## Advanced topics#

The input to `kwant.continuum.discretize`

and `kwant.continuum.lambdify`

can be
not only a `string`

, as we saw above, but also a `sympy`

expression or
a `sympy`

matrix.
This functionality will probably be mostly useful to people who
are already experienced with `sympy`

.

It is possible to use `identity`

(for identity matrix), `kron`

(for Kronecker product), as well as Pauli matrices `sigma_0`

,
`sigma_x`

, `sigma_y`

, `sigma_z`

in the input to
`lambdify`

and `discretize`

, in order to simplify
expressions involving matrices. Matrices can also be provided explicitly using
square `[]`

brackets. For example, all following expressions are equivalent:

```
sympify = kwant.continuum.sympify
subs = {'sx': [[0, 1], [1, 0]], 'sz': [[1, 0], [0, -1]]}
e = (
sympify('[[k_x**2, alpha * k_x], [k_x * alpha, -k_x**2]]'),
sympify('k_x**2 * sigma_z + alpha * k_x * sigma_x'),
sympify('k_x**2 * sz + alpha * k_x * sx', locals=subs),
)
print(e[0] == e[1] == e[2])
```

```
True
```

We can use the `locals`

keyword parameter to substitute expressions
and numerical values:

```
subs = {'A': 'A(x) + B', 'V': 'V(x) + V_0', 'C': 5}
print(sympify('k_x * A * k_x + V + C', locals=subs))
```

```
V_0 + 5 + k_x*(B + A(x))*k_x + V(x)
```

Symbolic expressions obtained in this way can be directly passed to all
`discretizer`

functions.

Technical details

Because of the way that `sympy`

handles commutation relations all symbols
representing position and momentum operators are set to be non commutative.
This means that the order of momentum and position operators in the input
expression is preserved. Note that it is not possible to define individual
commutation relations within `sympy`

, even expressions such \(x k_y x\)
will not be simplified, even though mathematically \([x, k_y] = 0\).

References