notebook/quantum-mechanics/1quantumwell.ipynb

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    "# **Numerical Solution of the Schrödinger Equation for a One-Dimensional Quantum Well**\n",
    "\n",
    "**Authors:** Dou Du, Taylor James Baird and Giovanni Pizzi\n",
    "\n",
    "<i class=\"fa fa-home fa-2x\"></i><a href=\"../index.ipynb\" style=\"font-size: 20px\"> Go back to index</a>\n",
    "\n",
    "**Source code:** https://github.com/osscar-org/quantum-mechanics/blob/master/notebook/quantum-mechanics/1quantumwell.ipynb\n",
    "\n",
    "This notebook solves numerically the quantum-mechanical problem of a single rectangular one-dimensional quantum well, and displays interactively the eigenfunctions (plotted at the height of the corresponding eigenvalues).\n",
    "\n",
    "<hr style=\"height:1px;border:none;color:#cccccc;background-color:#cccccc;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Goals**\n",
    "* Investigate quantum confinement by manipulating the depth and width of a finite one-dimensional quantum well.\n",
    "* Learn how to solve the Schrödinger equation by matrix diagonalization.\n",
    "* Understand quantum tunnelling in regions where the energy is lower than the potential."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Background theory** \n",
    "\n",
    "[More on the background theory.](./theory/theory_1quantumwell.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Tasks and exercises**\n",
    "\n",
    "1. Keep the depth at the default value (-0.2) and move the slider of the width from the\n",
    "smallest value (0.1) to the largest value (2.0). Do you see any change for the number\n",
    "of the states? How does the eigenvalue of the ground state (the lowest eigenstate) change \n",
    "with the increasing of the width of the quantum well and why?\n",
    "\n",
    "    <details>\n",
    "    <summary style=\"color: red\">Solution</summary>\n",
    "    <div style=\"border:blue; border-width:3px; border-style:outset;\">  \n",
    "        The wider the well potential is, the more (bound state) wavefunctions you will obtain. The reason for this can be traced back to the relationship between the eigenenergies of the bound states and the width of the well: $E_n \\propto \\frac{1}{L^2}$. Therefore, increasing the width of the well decreases the values of the various eigenergies. Note that the \"zero\" of the wavefunction is shifted, for every wavefunction, at the energy of the corresponding eigenvalue.\n",
    "    </div>\n",
    "    </details>\n",
    "    \n",
    "2. Keep the width at the default value (1.2) and move the slider of the depth from\n",
    "the smallest value (-1.0) to the largest value (0.0). Do you see any change for the \n",
    "number of the states? How does the eigenvalue of the ground state (the lowest eigenstate)\n",
    "change with the increasing of the depth of the quantum well and why?\n",
    "\n",
    "    <details>\n",
    "    <summary style=\"color: red\">Solution</summary>\n",
    "    <div style=\"border:blue; border-width:3px; border-style:outset;\">\n",
    "        The deeper the well potential is, the more wavefunctions you will obtain. Mathematically, the explanation for this lies in the relationship between the values of the eigenenergies and the potential depth. This is a transcendental relationship and has to be solved numerically or graphically but one may note that by increasing the well depth, the values of the various energy eigenvalues become progressively more negative. Some important remarks are that, regardless of the well depth, there is always at least one bound solution in one dimension if the well depth is greater than zero, and that as the depth is made progressively larger, the energy levels shall approach those of the infinite square well. For more information see \n",
    "                <a href=\"https://en.wikipedia.org/wiki/Finite_potential_well\">Wikipedia</a>.\n",
    "        Note that the \"zero\" of the wavefunction is shifted, for every wavefunction, \n",
    "        at the energy of the corresponding eigenvalue.\n",
    "    </div>\n",
    "    </details>\n",
    "\n",
    "3. Investigate the role played by quantum confinement in this 1D potential well system by varying the width of the well via the \"width\" slider and observing the spacing of the energy levels of the system.\n",
    "\n",
    "    <details>\n",
    "    <summary style=\"color: red\">Solution</summary>\n",
    "    <div style=\"border:blue; border-width:3px; border-style:outset;\">\n",
    "        When the dimension of the confining potential becomes comparable to the de Broglie wavelength of the confined particle then we begin to observe so-called confinement effects. These manifest as a discretization of the bound state energy levels (when the de Broglie wavelength and well width are not comparable the particle behaves essentially like a free particle, with a continuous energy spectrum). As mentioned in task 1, the relationship between the energy levels and the width of the well is $E_n \\propto \\frac{1}{L^2}$; consequently, as we decrease the width of the well, the spacing of the energy levels becomes larger and larger. As an application of this effect we note that in a nanostructure the effect of increased confinement (reducing the size of the structure) is to increase the band gap of that structure. This can be leveraged to manipulate the opto-electronic properties of the nanostructure. \n",
    "        Furthermore, we note that in a classical potential, the lowest energy state would be at the bottom of the well. In quantum mechanics, the lowest energy state has a higher energy \n",
    "        than the bottom of the quantum well, as you can see in the figure below. \n",
    "        The reason for this energy difference is what is typically called the quantum \n",
    "        confinement effect. Observe also how the lowest energy state changes with \n",
    "        the width of the potential well.\n",
    "    </div>\n",
    "    </details>\n",
    "    \n",
    "4. Keeping the sliders for the width and the height of the well at their default values and selecting the \"Probability density\" option, investigate the probability of a confined particle to observed outside the potential well.\n",
    "  <details>\n",
    "    <summary style=\"color: red\">Solution</summary>\n",
    "    <div style=\"border:blue; border-width:3px; border-style:outset;\">\n",
    "       One can see that even although the potential outside the well is greater than the energy of the particle, there exists a non-zero probability of finding said particle at a position outside the well. This possibility of the particle \"tunnelling\" through the potential barrier in this way is a purely quantum mechanical effect. In the classical picture of this system one would never expect to observe the particle outside the confines of the potential well.\n",
    "    </div>\n",
    "    </details>\n",
    "  \n",
    "5. Please read the background theory section and try to understand the core of the\n",
    "numerical algorithm. Why is the diagonalization of the Hamiltonian matrix key to \n",
    "solve the Schrödinger equation?\n",
    "\n",
    "    <details>\n",
    "    <summary style=\"color: red\">Solution</summary>\n",
    "    <div style=\"border:blue; border-width:3px; border-style:outset;\">\n",
    "        As shown in the background theory section, the Schrödinger equation is an \n",
    "        eigenvalue equation. In mathematics, diagonalization of the matrix allows us\n",
    "        to obtain its eigenvalues and eigenfunctions. You can read more \n",
    "        about the theory on \n",
    "        <a href=\"https://en.wikipedia.org/wiki/Diagonalizable_matrix\">Wikipedia</a>.\n",
    "    </div>\n",
    "    </details>\n",
    "\n",
    "<hr style=\"height:1px;border:none;color:#cccccc;background-color:#cccccc;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Interactive visualization**\n",
    "(be patient, it might take a few seconds to load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "from numpy import linspace, sqrt, ones, arange, diag, argsort, zeros\n",
    "from scipy.linalg import eigh_tridiagonal\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import FloatSlider, jslink, VBox, HBox, Button, Label, RadioButtons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928']\n",
    "ixx = 0\n",
    "\n",
    "def singlewell_potential(x, width, depth):\n",
    "    x1 = zeros(len(x))\n",
    "    for i in range(len(x)):\n",
    "        if x[i] > - width/2.0 and x[i] < width/2.0:\n",
    "            x1[i] = depth\n",
    "    return x1\n",
    "    \n",
    "\n",
    "def diagonalization(hbar, L, N, pot=singlewell_potential, width = 0.1, depth = -0.1):\n",
    "    \"\"\"Calculate sorted eigenvalues and eigenfunctions by diagonalization of the Hamiltonian matrix. \n",
    "\n",
    "       Input:\n",
    "         hbar: reduced Planck constant\n",
    "         L: set discretized interval as [-L,L] \n",
    "         N: number of grid points, i.e., size of the H matrix \n",
    "         pot: python function returning the value of the potential energy\n",
    "         x0: center of the quantum well\n",
    "         width: the width of the quantum well\n",
    "         depth: the depth of the quantum well\n",
    "       Ouput:\n",
    "         ew: sorted eigenvalues (array of length N)\n",
    "         ef: sorted eigenfunctions, ef[:,i] (size N*N)\n",
    "         x:  grid points (array of length N)\n",
    "         dx: grid spacing (called Delta in the theory above)\n",
    "         V:  values of the potential discretized on the grid x (array of length N)\n",
    "    \"\"\"\n",
    "    x = linspace(-L, L, N+2)[1:N+1]                 # grid points\n",
    "    dx = x[1] - x[0]                                # grid spacing\n",
    "    V = pot(x, width, depth)\n",
    "    z = hbar**2 /2.0/dx**2                         # coefficient\n",
    "\n",
    "    ew, ef = eigh_tridiagonal(V+2.0*z, -z*ones(N-1))\n",
    "    ew = ew.real                                    # real part of the eigenvalues\n",
    "    ind = argsort(ew)                               # Indices f. sort. Array\n",
    "    ew = ew[ind]                                    # Sort the ew by ind\n",
    "    ef = ef[:, ind]                                 # Sort the columns \n",
    "    ef = ef/sqrt(dx)                                # Correct standardization \n",
    "    return ew, ef, x, dx, V\n",
    "\n",
    "\n",
    "def plot_eigenfunctions(ax, ew, ef, x, V, width=1, Emax=0.05, fak= 5.0):\n",
    "    \"\"\"Create the full plot.\n",
    "    \n",
    "    Plot the lowest squared eigenfunctions 'ef' at the level of the eigenvalues\n",
    "    'ew' in the plot area 'ax', and the potential 'V(x)'.\n",
    "    \"\"\"\n",
    "    if psi_x.value == \"Wavefunction\":\n",
    "        y_text = r'$V(x)$ and $\\psi(x)$'\n",
    "\n",
    "        fig.suptitle('Numerical Solution ($\\psi$) Of The One Dimensional Schrödinger Equation', fontsize = 13)\n",
    "    else:\n",
    "        y_text = r'$V(x)$ and $|\\psi(x)|^2$'\n",
    "        fig.suptitle('Numerical Solution ($\\psi^2$) Of The One Dimensional Schrödinger Equation', fontsize = 13)\n",
    "        \n",
    "    fak = fak/100.0\n",
    "    \n",
    "    ax[0].axhspan(0.0, Emax, facecolor='lightgrey')\n",
    "    \n",
    "    ax[0].set_xlim([min(x), max(x)])\n",
    "    ax[0].set_ylim([min(V)-0.05, Emax])\n",
    "    \n",
    "    ax[0].set_xlabel(r'$x/a$', fontsize = 10)\n",
    "    ax[0].set_ylabel(y_text, fontsize = 10)\n",
    "    \n",
    "    ax[1].set_xlim([min(x), max(x)])\n",
    "    ax[1].set_ylim([min(V)-0.05, Emax])\n",
    "    \n",
    "    ax[1].yaxis.set_label_position(\"right\")\n",
    "    ax[1].yaxis.tick_right()\n",
    "    \n",
    "    ax[1].get_xaxis().set_visible(False)\n",
    "    ax[1].set_ylabel(r'$\\rm{\\ Eigenvalues}$', fontsize = 10)\n",
    "    \n",
    "    indmax = sum(ew<=0.0)                       \n",
    "    if not hasattr(width, \"__iter__\"):           \n",
    "        width = width*ones(indmax)               \n",
    "    for i in arange(indmax):                     \n",
    "        if psi_x.value == \"Wavefunction\":\n",
    "            ax[0].plot(x, fak*(ef[:, i])+ew[i], linewidth=width[i]+.1, color=colors[i%len(colors)])\n",
    "        else:\n",
    "            ax[0].plot(x, fak*abs(ef[:, i])**2+ew[i], linewidth=width[i]+.1, color=colors[i%len(colors)])\n",
    "\n",
    "        ax[1].plot(x, x*0.0+ew[i], linewidth=width[i]+2.5, color=colors[i%len(colors)])\n",
    "        \n",
    "    ax[0].plot(x, V, c='k', linewidth=1.6)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=700.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(header_visible=False, layout=Layout(width='750px'), toolbar=Toolbar(toolitems=[('Home', 'Reset original…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9aa573d792e44e03a0a1a1bfd8254087",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatSlider(value=1.2, description='Width: ', max=2.0, min=0.1), FloatSlider(value=-0.2, descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e46123893144339b6908e8d774ffa1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=5.0, description='Zoom factor: ', max=10.0, min=1.0, step=1.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31134e6c6d044de183a2926f58a4421d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(RadioButtons(description='Show:', options=('Wavefunction', 'Probability density'), value='Wavef…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 0.06                                            # Potential parameter\n",
    "L = 1.5                                              # x range [-L,L]\n",
    "N = 200                                              # Number of grid points\n",
    "hbar = 0.06                                          # Reduced Planck constant\n",
    "sigma_x = 0.1                                        # Width of the Gaussian function\n",
    "zeiten = linspace(0.0, 10.0, 400)                    # time\n",
    "\n",
    "\n",
    "swidth = FloatSlider(value = 1.2, min = 0.1, max = 2.0, description = 'Width: ')\n",
    "sdepth = FloatSlider(value = -0.2, min = -1.0, max = -0.1, step = 0.05, description = 'Depth: ')\n",
    "sfak = FloatSlider(value = 5, min = 1.0, max = 10.0, step = 1.0, description = r'Zoom factor: ')\n",
    "update = Button(description=\"Show all\")\n",
    "psi_x = RadioButtons(options=[\"Wavefunction\", \"Probability density\"], value=\"Wavefunction\", description=\"Show:\")\n",
    "\n",
    "width = 1.2\n",
    "depth = -0.2\n",
    "fak = 5.0\n",
    "\n",
    "ew, ef, x, dx, V = diagonalization(hbar, L, N, width = width, depth = depth)\n",
    "    \n",
    "fig, ax = plt.subplots(1, 2, figsize=(7,5), gridspec_kw={'width_ratios': [10, 1]})\n",
    "fig.canvas.header_visible = False\n",
    "fig.canvas.layout.width = \"750px\"\n",
    "\n",
    "fig.suptitle(r'Numerical Solution ($\\psi^2$) of the Schrödinger Equation for a 1D Quantum Well', fontsize = 12)\n",
    "plot_eigenfunctions(ax, ew, ef, x, V)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "def on_update_click(b):\n",
    "    for i in ax[0].lines:\n",
    "        i.set_alpha(1.0)\n",
    "    for i in ax[1].lines:\n",
    "        i.set_alpha(1.0)\n",
    "    try:\n",
    "        ann.remove()\n",
    "        ann1.remove()\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "def on_width_change(change):\n",
    "    global ew, ef, x, dx, V\n",
    "    ax[0].clear()\n",
    "    ax[1].clear()\n",
    "    \n",
    "    try:\n",
    "        ann.remove()\n",
    "        ann1.remove()\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    ew, ef, x, dx, V = diagonalization(hbar, L, N, width = swidth.value, depth = sdepth.value)\n",
    "    plot_eigenfunctions(ax, ew, ef, x, V, fak = sfak.value)\n",
    "\n",
    "def on_depth_change(change):\n",
    "    global ew, ef, x, dx, V\n",
    "    ax[0].clear()\n",
    "    ax[1].clear()\n",
    "    \n",
    "    try:\n",
    "        ann.remove()\n",
    "        ann1.remove()\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    ew, ef, x, dx, V = diagonalization(hbar, L, N, width = swidth.value, depth = sdepth.value)\n",
    "    plot_eigenfunctions(ax, ew, ef, x, V, fak = sfak.value)\n",
    "    \n",
    "def on_xfak_change(change):\n",
    "    ax[0].clear()\n",
    "    ax[1].clear()\n",
    "    \n",
    "    try:\n",
    "        ann.remove()\n",
    "        ann1.remove()\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    plot_eigenfunctions(ax, ew, ef, x, V, fak = sfak.value)\n",
    "\n",
    "def on_press(event):\n",
    "    global ann, ann1, ixx\n",
    "    \n",
    "    ixx = min(enumerate(ew), key = lambda x: abs(x[1]-event.ydata))[0]\n",
    "    \n",
    "    for i in range(len(ax[1].lines)):\n",
    "        ax[0].lines[i].set_alpha(0.1)\n",
    "        ax[1].lines[i].set_alpha(0.1)\n",
    "        ax[0].lines[i].set_linewidth(1.1)\n",
    "        \n",
    "    ax[0].lines[ixx].set_alpha(1.0)\n",
    "    ax[1].lines[ixx].set_alpha(1.0)\n",
    "    ax[0].lines[ixx].set_linewidth(2.0)\n",
    "    \n",
    "    try:\n",
    "        ann.remove()\n",
    "        ann1.remove()\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    ann = ax[0].annotate(s = 'n = ' + str(ixx+1), xy = (0, ew[ixx]), xytext = (-0.15, ew[ixx]), xycoords = 'data', color='k', size=15)\n",
    "    ann1 = ax[1].annotate(s = str(\"{:.3f}\".format(ew[ixx])), xy = (0, ew[ixx]), xytext = (-1.2, ew[ixx]+0.005), xycoords = 'data', color='k', size=9)\n",
    "\n",
    "cid = fig.canvas.mpl_connect('button_press_event', on_press)\n",
    "\n",
    "swidth.observe(on_width_change, names = 'value')\n",
    "sdepth.observe(on_depth_change, names = 'value')\n",
    "sfak.observe(on_xfak_change, names = 'value')\n",
    "update.on_click(on_update_click)\n",
    "psi_x.observe(on_width_change, names = 'value')\n",
    "\n",
    "label1 = Label(value=\"(click on a state to select it)\");\n",
    "\n",
    "display(HBox([swidth, sdepth]), sfak, HBox([psi_x, update, label1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Width:** the width of the quantum well.\n",
    "* **Depth:** the depth of the quantum well.\n",
    "* **Zoom factor:** the zoom factor for the magnitude of the eigenfunctions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;color:#cccccc;background-color:#cccccc;\" />\n",
    "\n",
    "## **Legend**\n",
    "\n",
    "(How to use the interactive visualization)\n",
    "\n",
    "### Controls\n",
    "There are three sliders you can tune interactively. Two of them adjust \n",
    "the width and depth of the potential well. The zoom factor slider is used \n",
    "to adjust the normalization factor of the wavefunctions. This factor is used for visualization \n",
    "purposes only, to avoid overlap of the wavefunctions whilst simultaneously preventing the wavefunctions from being so narrow that you cannot appreciate their shape.\n",
    "\n",
    "### Interactive figure\n",
    "In the main visualization part, there are two subplots. The wide figure on \n",
    "the left shows the well potential and the square moduli of the eigenfunctions \n",
    "$|\\psi|^2$ of different states, plotted at the energy of the corresponding \n",
    "eigenvalues. The narrow figure on the right shows the eigenvalues.\n",
    "You can select either an eigenfunction or an eigenvalue by clicking on \n",
    "it in one of the two subplots. The eigenfunction and corresponding eigenvalue \n",
    "will be highlighted, and the corresponding eigenenergy and value of the index \n",
    "$n$ will be shown. All other eigenstates will fade out. By clicking the \"Show all\" \n",
    "button, you can reset the plot and show all states again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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