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Bowy La Riviere authoredBowy La Riviere authored
from matplotlib import pyplot as plt
import numpy as np
from math import pi
Solutions for lecture 10 exercises
Warm-up exercises
??? hint "Small hint"
You can make use of the [scalar triple product](https://en.wikipedia.org/wiki/Triple_product#Scalar_triple_product).
If
No, there is a single atom, and thus only one term in the structure factor. This results in only a single exponent being present in the structure factor, which is always nonzero.
No, an increase of the unit cell size cannot create new diffraction peaks (see lecture).
Exercise 1: Equivalence of direct and reciprocal lattice
In the second equality, we used the reciprocal lattice vector definition (see notes). In the third equality, we used the identity:
Because the relation between direct and reciprocal lattice is symmetric, so are the expressions for the direct lattice vectors through the reciprocal ones:
where
One set of the BCC primitive lattice vectors is given by:
From this, we find the following set of reciprocal lattice vectrs:
which is forms a reciprocal FCC lattice. The opposite relation follows directly from our previous result.
Because the 1st Brillouin Zone is the Wigner-Seitz cell of the reciprocal lattice, we need to construct the Wigner-Seitz cell of the FCC lattice. For visualization, it is convenient to look at FCC lattice introduced in the previous lecture and count the neirest neighbours of each lattice point. We see that each lattice point contains 12 neirest neighbours and thus the Wigner-Seitz cell contains 12 sides!
Exercise 2: Miller planes and reciprocal lattice vectors
??? hint "First small hint"
The $(hkl)$ plane intersects lattice at position vectors of $\frac{\mathbf{a_1}}{h}, \frac{\mathbf{a_2}}{k}, \frac{\mathbf{a_3}}{l}$.
Can you define a general vector inside the $(hkl)$ plane?
??? hint "Second small hint"
Whats the best vector operation to show orthogonality between two vectors?
One can compute the normal to the plane by using result from Subquestion 1:
Let us consider a very simple case in which we have the miller planes
$ d = \hat{\mathbf{n}} \cdot \frac{\mathbf{a_1}}{h} = \frac{2 \pi}{|G|} $
Since
Exercise 3: X-ray scattering in 2D
def reciprocal_lattice(N = 7, lim = 40):
y = np.repeat(np.linspace(-18.4*(N//2),18.4*(N//2),N),N)
x = np.tile(np.linspace(-13.4*(N//2),13.4*(N//2),N),N)
plt.figure(figsize=(5,5))
plt.plot(x,y,'o', markersize=10, markerfacecolor='none', color='k')
plt.xlim([-lim,lim])
plt.ylim([-lim,lim])
plt.xlabel('$\mathbf{b_1}$')
plt.ylabel('$\mathbf{b_2}$')
plt.xticks(np.linspace(-lim,lim,5))
plt.yticks(np.linspace(-lim,lim,5))
reciprocal_lattice()
plt.show()
Since we have elastic scattering, we obtain
reciprocal_lattice()
# G vector
plt.arrow(0,0,13.4*2,18.4,color='r',zorder=10,head_width=2,length_includes_head=True)
plt.annotate('$\mathbf{G}$',(17,6.5),fontsize=14,ha='center',color='r')
# k vector
plt.arrow(-6,37.4,6,-37.4,color='b',zorder=11,head_width=2,length_includes_head=True)
plt.annotate('$\mathbf{k}$',(-8,18),fontsize=14, ha='center',color='b')
# k' vector
plt.arrow(-6,37.4,6+13.4*2,-37.4+18.4,color='k',zorder=11,head_width=2,length_includes_head=True)
plt.annotate('$\mathbf{k\'}$',(15,30),fontsize=14, ha='center',color='k')
plt.show()
Exercise 4: Structure factors
Solving for
$ S(\mathbf{G}) = \begin{cases} 2f, : \text{if
Thus if
Let
$ S(\mathbf{G}) = \begin{cases} f_1 + f_2, \text{if
$
Due to bcc systematic absences, the peaks from lowest to largest angle are: