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              {
                "output_type": "stream",
                "name": "stderr",
                "text": [
                  "/tmp/ipykernel_12135/1690787019.py:89: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
                  "  plt.tight_layout()\n"
                ]
              },
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                "data": {
                  "text/plain": "<Figure size 1650x550 with 3 Axes>",
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\n"
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              },
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "\n",
                  "────────────────────────────────────────────────────\n",
                  "  Temperatura T = 1.00\n",
                  "  Energía de punto cero ZPE         = 8.9846\n",
                  "  Energía total ⟨U⟩                 = 20.6266\n",
                  "  Calor específico Cv               = 17.4505\n",
                  "  Límite clásico N·kB               = 19.0000\n",
                  "  Fracción Cv / (N·kB)              = 0.9184\n",
                  "────────────────────────────────────────────────────\n"
                ]
              }
            ]
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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Simulación: Energía total y calor específico del sistema de fonones\n",
        "\n",
        "### Modelo físico\n",
        "\n",
        "La energía total cuantizada del sistema (Kittel, ec. 21) es:\n",
        "\n",
        "$$U = \\sum_k \\left( n_k + \\frac{1}{2} \\right) \\hbar\\omega_k$$\n",
        "\n",
        "En equilibrio térmico a temperatura $T$, la **distribución de Planck** da la ocupación media de cada modo:\n",
        "\n",
        "$$\\langle n_k \\rangle = \\frac{1}{e^{\\hbar\\omega_k / k_B T} - 1}$$\n",
        "\n",
        "La energía promedio del sistema es entonces:\n",
        "\n",
        "$$\\langle U \\rangle = \\sum_k \\hbar\\omega_k \\left( \\frac{1}{e^{\\hbar\\omega_k / k_B T} - 1} + \\frac{1}{2} \\right)$$\n",
        "\n",
        "El **calor específico a volumen constante** se obtiene por diferenciación:\n",
        "\n",
        "$$C_v = \\frac{\\partial \\langle U \\rangle}{\\partial T} = k_B \\sum_k \\left( \\frac{\\hbar\\omega_k}{k_B T} \\right)^2 \\frac{e^{\\hbar\\omega_k/k_BT}}{\\left(e^{\\hbar\\omega_k/k_BT} - 1\\right)^2}$$\n",
        "\n",
        "### Límites conocidos\n",
        "\n",
        "- **Alta temperatura** ($k_B T \\gg \\hbar\\omega_k$): recuperamos el resultado clásico de equipartición, $C_v \\to Nk_B$ (ley de Dulong-Petit).\n",
        "- **Baja temperatura** ($k_B T \\ll \\hbar\\omega_k$): $C_v \\to 0$, comportamiento cuántico.\n",
        "\n",
        "La simulación usa la relación de dispersión exacta de la cadena 1D (no el modelo de Debye ni el de Einstein)."
      ],
      "metadata": {
        "id": "cDU05zsbpYNM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importaciones y configuración"
      ],
      "metadata": {
        "id": "ebWRRMkBp4vR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "from matplotlib.gridspec import GridSpec\n",
        "from ipywidgets import interact, interactive, fixed, widgets, HBox, VBox, Output\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "# Constantes físicas\n",
        "HBAR = 1.0545718e-34   # J·s\n",
        "KB   = 1.380649e-23    # J/K\n",
        "\n",
        "plt.rcParams.update({\n",
        "    'figure.dpi': 110,\n",
        "    'axes.titlesize': 13,\n",
        "    'axes.labelsize': 12,\n",
        "    'font.family': 'serif'\n",
        "})\n",
        "print('Entorno listo ✓')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7Gn7bXqp1TB",
        "outputId": "bf84dafc-d315-4081-f6e5-7c249ba06d4d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entorno listo ✓\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ─── Funciones auxiliares para la Simulación 3 ───────────────────────────────\n",
        "\n",
        "def dispersion(k_vals, C, M, a=1.0):\n",
        "    \"\"\"Relación de dispersión: ωk = sqrt(2C/M) * |sin(ka/2)| (Kittel ec.15)\"\"\"\n",
        "    return np.sqrt(2 * C / M) * np.abs(np.sin(k_vals * a / 2))\n",
        "\n",
        "\n",
        "def compute_displacements(s_arr, t, k_vals, omega_vals, amplitudes, phases, C, M, a=1.0):\n",
        "    \"\"\"\n",
        "    Calcula q_s(t) = 2 * Σ_k A_k * sqrt(ℏ/2NMωk) * cos(k·s·a - ωk·t + φk)\n",
        "    En unidades naturales (ℏ=1) para visualización.\n",
        "    El factor de amplitud cuántica (ℏ/2NMωk)^(1/2) está incluido.\n",
        "    \"\"\"\n",
        "    N = len(s_arr)\n",
        "    q = np.zeros(N)\n",
        "    for k, omega, A, phi in zip(k_vals, omega_vals, amplitudes, phases):\n",
        "        if omega < 1e-10:  # modo k=0 no oscila\n",
        "            continue\n",
        "        # Factor cuántico de amplitud (unidades arbitrarias normalizadas)\n",
        "        q_amp = A / np.sqrt(omega)\n",
        "        q += 2 * q_amp * np.cos(k * s_arr * a - omega * t + phi)\n",
        "    return q"
      ],
      "metadata": {
        "id": "c8wbzeaBqFOI"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ─── Funciones auxiliares para la Simulación  ───────────────────────────────\n",
        "\n",
        "def get_all_modes(N, C, M, a=1.0):\n",
        "    \"\"\"Devuelve todos los vectores de onda y frecuencias permitidos.\"\"\"\n",
        "    ns = np.arange(-N // 2 + 1, N // 2 + 1)  # N modos en total\n",
        "    k_vals = 2 * np.pi * ns / (N * a)\n",
        "    omega_vals = dispersion(k_vals, C, M, a)\n",
        "    # Excluir el modo k=0 (omega=0, contribuye solo ZPE constante)\n",
        "    omega_vals = omega_vals[omega_vals > 1e-12]\n",
        "    return omega_vals\n",
        "\n",
        "\n",
        "def planck_occupancy(omega, T, hbar=1.0, kB=1.0):\n",
        "    \"\"\"Distribución de Planck: <nk> = 1 / (exp(ℏωk/kBT) - 1)\"\"\"\n",
        "    x = hbar * omega / (kB * T)\n",
        "    # Evitar overflow para x muy grande\n",
        "    return np.where(x > 500, 0.0, 1.0 / (np.expm1(x)))\n",
        "\n",
        "\n",
        "def total_energy(T_arr, omega_vals, hbar=1.0, kB=1.0):\n",
        "    \"\"\"U(T) = Σk ℏωk * (<nk> + 1/2) para un array de temperaturas.\"\"\"\n",
        "    U = np.zeros_like(T_arr, dtype=float)\n",
        "    ZPE = 0.5 * np.sum(hbar * omega_vals)  # energía de punto cero (cte)\n",
        "    for i, T in enumerate(T_arr):\n",
        "        if T < 1e-10:\n",
        "            U[i] = ZPE\n",
        "        else:\n",
        "            nk = planck_occupancy(omega_vals, T, hbar, kB)\n",
        "            U[i] = np.sum(hbar * omega_vals * (nk + 0.5))\n",
        "    return U\n",
        "\n",
        "\n",
        "def heat_capacity(T_arr, omega_vals, hbar=1.0, kB=1.0):\n",
        "    \"\"\"Cv(T) = kB Σk (ℏωk/kBT)² * exp(x)/(exp(x)-1)²\"\"\"\n",
        "    Cv = np.zeros_like(T_arr, dtype=float)\n",
        "    for i, T in enumerate(T_arr):\n",
        "        if T < 1e-10:\n",
        "            Cv[i] = 0.0\n",
        "        else:\n",
        "            x = hbar * omega_vals / (kB * T)\n",
        "            # Cálculo numéricamente estable\n",
        "            safe = x < 500\n",
        "            ex = np.where(safe, np.exp(x), 0.0)\n",
        "            contrib = np.where(safe,\n",
        "                               kB * x**2 * ex / (ex - 1)**2,\n",
        "                               0.0)\n",
        "            Cv[i] = np.sum(contrib)\n",
        "    return Cv"
      ],
      "metadata": {
        "id": "50AQ92c5pTlr"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ─── Simulación Interactiva ─────────────────────────────────────────────────\n",
        "#\n",
        "# Controles:\n",
        "#   - N: número de partículas (modos)\n",
        "#   - C, M: parámetros de la cadena\n",
        "#   - T_max: temperatura máxima del gráfico\n",
        "#   - T_mark: temperatura de trabajo (marcador vertical)\n",
        "\n",
        "def plot_thermodynamics(N=20, C=1.0, M=1.0, T_max=5.0, T_mark=1.0):\n",
        "\n",
        "    # En unidades naturales: ℏ = kB = 1, a = 1\n",
        "    hbar, kB, a = 1.0, 1.0, 1.0\n",
        "\n",
        "    omega_vals = get_all_modes(N, C, M, a)\n",
        "    omega_max  = np.max(omega_vals)\n",
        "    N_modes    = len(omega_vals)\n",
        "\n",
        "    T_arr = np.linspace(0.01, T_max, 400)\n",
        "    U_arr = total_energy(T_arr, omega_vals, hbar, kB)\n",
        "    Cv_arr = heat_capacity(T_arr, omega_vals, hbar, kB)\n",
        "\n",
        "    # Energía de punto cero\n",
        "    ZPE = 0.5 * np.sum(hbar * omega_vals)\n",
        "\n",
        "    # Límite clásico Dulong-Petit: Cv = N_modes * kB\n",
        "    Cv_classical = N_modes * kB\n",
        "\n",
        "    # ── Figura con 3 paneles ──\n",
        "    fig = plt.figure(figsize=(15, 5))\n",
        "    gs  = GridSpec(1, 3, figure=fig, wspace=0.35)\n",
        "    ax1 = fig.add_subplot(gs[0])\n",
        "    ax2 = fig.add_subplot(gs[1])\n",
        "    ax3 = fig.add_subplot(gs[2])\n",
        "\n",
        "    # ── Panel 1: Densidad de estados (histograma de frecuencias) ──\n",
        "    ax1.hist(omega_vals, bins=max(N_modes // 3, 8),\n",
        "             color='#2E86AB', alpha=0.8, edgecolor='white', lw=0.5)\n",
        "    ax1.set_xlabel('$\\\\omega_k$ (u.a.)')\n",
        "    ax1.set_ylabel('Número de modos')\n",
        "    ax1.set_title('Distribución de frecuencias\\n(Densidad de estados 1D)')\n",
        "    ax1.axvline(omega_max, color='#E84855', ls='--', lw=1.3,\n",
        "                label=f'$\\\\omega_{{max}} = {omega_max:.3f}$')\n",
        "    ax1.legend(fontsize=9)\n",
        "\n",
        "    # ── Panel 2: Energía total U(T) ──\n",
        "    ax2.plot(T_arr, U_arr, '#1A1A2E', lw=2.2, label='$\\\\langle U(T) \\\\rangle$')\n",
        "    ax2.axhline(ZPE, color='#3BB273', ls=':', lw=1.5,\n",
        "                label=f'ZPE = {ZPE:.2f}')\n",
        "    ax2.axvline(T_mark, color='#E84855', ls='--', lw=1.2,\n",
        "                label=f'$T$ = {T_mark:.2f}')\n",
        "\n",
        "    # Marcar U en T_mark\n",
        "    U_at_T = float(total_energy(np.array([T_mark]), omega_vals, hbar, kB)[0])\n",
        "    ax2.scatter([T_mark], [U_at_T], color='#E84855', s=60, zorder=5)\n",
        "    ax2.annotate(f'U={U_at_T:.2f}', xy=(T_mark, U_at_T),\n",
        "                 xytext=(T_mark + T_max * 0.04, U_at_T),\n",
        "                 fontsize=9, color='#E84855')\n",
        "\n",
        "    ax2.set_xlabel('Temperatura $T$ (u.a.)')\n",
        "    ax2.set_ylabel('Energía $\\\\langle U \\\\rangle$ (u.a.)')\n",
        "    ax2.set_title('Energía total (Kittel ec. 21)\\n$\\\\langle U\\\\rangle = \\\\sum_k \\\\hbar\\\\omega_k(\\\\langle n_k\\\\rangle + \\\\frac{1}{2})$')\n",
        "    ax2.legend(fontsize=9)\n",
        "\n",
        "    # ── Panel 3: Calor específico Cv(T) ──\n",
        "    ax3.plot(T_arr, Cv_arr, '#2E86AB', lw=2.2, label='$C_v(T)$ (cadena 1D exacta)')\n",
        "    ax3.axhline(Cv_classical, color='#F4A261', ls='--', lw=1.5,\n",
        "                label=f'Dulong-Petit: $Nk_B = {Cv_classical:.1f}$')\n",
        "    ax3.axvline(T_mark, color='#E84855', ls='--', lw=1.2,\n",
        "                label=f'$T$ = {T_mark:.2f}')\n",
        "\n",
        "    # Marcar Cv en T_mark\n",
        "    Cv_at_T = float(heat_capacity(np.array([T_mark]), omega_vals, hbar, kB)[0])\n",
        "    ax3.scatter([T_mark], [Cv_at_T], color='#E84855', s=60, zorder=5)\n",
        "    ax3.annotate(f'$C_v$={Cv_at_T:.2f}', xy=(T_mark, Cv_at_T),\n",
        "                 xytext=(T_mark + T_max * 0.04, Cv_at_T),\n",
        "                 fontsize=9, color='#E84855')\n",
        "\n",
        "    ax3.set_xlabel('Temperatura $T$ (u.a.)')\n",
        "    ax3.set_ylabel('$C_v$ (unidades de $k_B$)')\n",
        "    ax3.set_title('Calor específico\\n$C_v = \\\\partial\\\\langle U\\\\rangle / \\\\partial T$')\n",
        "    ax3.legend(fontsize=9)\n",
        "    ax3.set_ylim(0, Cv_classical * 1.15)\n",
        "\n",
        "    plt.suptitle(\n",
        "        f'Cadena 1D: $N={N}$ partículas, $C={C}$, $M={M}$  —  '\n",
        "        f'$\\\\omega_{{max}} = {omega_max:.3f}$, {N_modes} modos',\n",
        "        fontsize=12, y=1.02\n",
        "    )\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Tabla de ocupaciones para T_mark\n",
        "    print(f\"\\n{'─'*52}\")\n",
        "    print(f\"  Temperatura T = {T_mark:.2f}\")\n",
        "    print(f\"  Energía de punto cero ZPE         = {ZPE:.4f}\")\n",
        "    print(f\"  Energía total ⟨U⟩                 = {U_at_T:.4f}\")\n",
        "    print(f\"  Calor específico Cv               = {Cv_at_T:.4f}\")\n",
        "    print(f\"  Límite clásico N·kB               = {Cv_classical:.4f}\")\n",
        "    print(f\"  Fracción Cv / (N·kB)              = {Cv_at_T / Cv_classical:.4f}\")\n",
        "    print(f\"{'─'*52}\")\n",
        "\n",
        "\n",
        "# ── Widgets ──\n",
        "style = {'description_width': 'initial'}\n",
        "\n",
        "interact(\n",
        "    plot_thermodynamics,\n",
        "    N=widgets.IntSlider(value=20, min=4, max=60, step=2,\n",
        "                        description='N (modos)', style=style),\n",
        "    C=widgets.FloatSlider(value=1.0, min=0.1, max=5.0, step=0.1,\n",
        "                          description='C (rigidez)', style=style),\n",
        "    M=widgets.FloatSlider(value=1.0, min=0.1, max=5.0, step=0.1,\n",
        "                          description='M (masa)', style=style),\n",
        "    T_max=widgets.FloatSlider(value=5.0, min=0.5, max=20.0, step=0.5,\n",
        "                              description='T máx', style=style),\n",
        "    T_mark=widgets.FloatSlider(value=1.0, min=0.01, max=10.0, step=0.05,\n",
        "                               description='T marcador', style=style)\n",
        ");"
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        "outputId": "bc672415-eec4-4b59-8c07-5407739bef8a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(IntSlider(value=20, description='N (modos)', max=60, min=4, step=2, style=SliderStyle(de…"
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    {
      "cell_type": "markdown",
      "source": [
        "### Guía de uso\n",
        "\n",
        "| Control | Efecto físico |\n",
        "|---|---|\n",
        "| **N** | Número de partículas/modos; más modos → curva $C_v(T)$ más suave |\n",
        "| **C** | Rigidez del resorte; sube $\\omega_{\\max}$ → se necesita más $T$ para alcanzar el límite clásico |\n",
        "| **M** | Masa de la partícula; baja $\\omega_{\\max}$, el efecto es inverso a $C$ |\n",
        "| **T máx** | Rango de temperatura del eje horizontal |\n",
        "| **T marcador** | Temperatura de trabajo: se muestra $U(T)$ y $C_v(T)$ numéricos |\n",
        "\n",
        "**Experimentos sugeridos:**\n",
        "\n",
        "1. **Límite clásico:** sube $T_{\\text{mark}}$ hasta que $C_v \\approx N k_B$. Observa que coincide con Dulong-Petit.\n",
        "2. **Efecto de $C$ y $M$:** duplica $C$ → $\\omega_{\\max}$ sube × $\\sqrt{2}$; para recuperar el 90% de $Nk_B$ necesitas el doble de $T$.\n",
        "3. **Energía de punto cero:** fija $T \\to 0$: $\\langle U \\rangle \\to \\text{ZPE} = \\frac{1}{2}\\sum_k \\hbar\\omega_k > 0$. No hay análogo clásico.\n",
        "4. **Dispersión 1D vs Debye:** en 1D la densidad de estados se apila en $\\omega_{\\max}$ (van Hove), produciendo una subida más lenta de $C_v$ que el modelo de Debye 3D."
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      "source": [
        "---\n",
        "## Resumen de ecuaciones del Kittel utilizadas\n",
        "\n",
        "| Nº ec. | Expresión | Uso |\n",
        "|:---:|---|---|\n",
        "| **1** | $H = \\sum_s \\left[\\frac{p_s^2}{2M} + \\frac{C}{2}(q_{s+1}-q_s)^2\\right]$ | Hamiltoniano de la cadena |\n",
        "| **6** | $k = 2\\pi n / Na$ | Modos permitidos por C.C.P. |\n",
        "| **15** | $\\omega_k = \\left(\\frac{2C}{M}\\right)^{1/2}|\\sin(ka/2)|$ | Relación de dispersión (Sim. 3 y 5) |\n",
        "| **20** | $\\varepsilon_k = (n_k + \\frac{1}{2})\\hbar\\omega_k$ | Energía de un fonón en modo $k$ |\n",
        "| **21** | $U = \\sum_k (n_k + \\frac{1}{2})\\hbar\\omega_k$ | Energía total del sistema (Sim. 5) |\n",
        "| **32** | $q_s = \\sum_k \\left(\\frac{\\hbar}{2NM\\omega_k}\\right)^{1/2}[a_k e^{iksa} + a_k^\\dagger e^{-iksa}]$ | Desplazamiento en términos de operadores fonónicos (Sim. 3) |\n",
        "\n",
        "---\n",
        "*Notebook elaborado con base en: C. Kittel, \"Introduction to Solid State Physics\", 8ª ed., Apéndice C.*"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Resumen de ecuaciones del Kittel utilizadas\n",
        "\n",
        "| Nº ec. | Expresión | Uso |\n",
        "|:---:|---|---|\n",
        "| **1** | $H = \\sum_s \\left[\\frac{p_s^2}{2M} + \\frac{C}{2}(q_{s+1}-q_s)^2\\right]$ | Hamiltoniano de la cadena |\n",
        "| **6** | $k = 2\\pi n / Na$ | Modos permitidos por C.C.P. |\n",
        "| **15** | $\\omega_k = \\left(\\frac{2C}{M}\\right)^{1/2}|\\sin(ka/2)|$ | Relación de dispersión (Sim. 3 y 5) |\n",
        "| **20** | $\\varepsilon_k = (n_k + \\frac{1}{2})\\hbar\\omega_k$ | Energía de un fonón en modo $k$ |\n",
        "| **21** | $U = \\sum_k (n_k + \\frac{1}{2})\\hbar\\omega_k$ | Energía total del sistema (Sim. 5) |\n",
        "| **32** | $q_s = \\sum_k \\left(\\frac{\\hbar}{2NM\\omega_k}\\right)^{1/2}[a_k e^{iksa} + a_k^\\dagger e^{-iksa}]$ | Desplazamiento en términos de operadores fonónicos (Sim. 3) |\n",
        "\n",
        "---\n",
        "*Notebook elaborado con base en: C. Kittel, \"Introduction to Solid State Physics\", 8ª ed., Apéndice C.*"
      ],
      "metadata": {
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}