diff --git a/examples/data/diatomic_molecule_example.nc b/examples/data/diatomic_molecule_example.nc
index 72606ebef5db3c368507c337ae52eae4c014103b..ce12b5654e9af2fa8dc3f48c0027c55bcd229af5 100644
Binary files a/examples/data/diatomic_molecule_example.nc and b/examples/data/diatomic_molecule_example.nc differ
diff --git a/examples/diatomic_molecule.ipynb b/examples/diatomic_molecule.ipynb
index 4b6a18f515a72166e1e4361160db4829ccfff1f1..990541272b1895f671c55088e61003e9d2c878de 100644
--- a/examples/diatomic_molecule.ipynb
+++ b/examples/diatomic_molecule.ipynb
@@ -12,7 +12,7 @@
     "import kwant\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from codes import utils, hf, kwant_examples\n",
+    "from codes import utils, hf, model\n",
     "from tqdm import tqdm\n",
     "from itertools import product"
    ]
@@ -98,27 +98,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 88,
    "id": "41bd9f60-8f29-4e7c-a0c4-a0bbf66445b2",
    "metadata": {},
    "outputs": [],
    "source": [
     "def compute_vals(\n",
-    "    tb_model,\n",
-    "    int_model,\n",
+    "    model,\n",
     "    filling=2,\n",
     "):\n",
     "\n",
     "    # Run SCF loop to find groundstate Hamiltonian.\n",
     "    scf_model = hf.find_groundstate_ham(\n",
-    "        tb_model=tb_model,\n",
-    "        int_model=int_model,\n",
+    "        model,\n",
     "        filling=filling,\n",
-    "        tol=1e-4,\n",
-    "        nk=2,\n",
+    "        solver=hf.finite_system_solver,\n",
+    "        cutoff_Vk=0,\n",
+    "        optimizer_kwargs={'M':2},\n",
     "    )\n",
     "    # Diagonalize groundstate Hamiltonian.\n",
-    "    vals, vecs = np.linalg.eigh(scf_model[next(iter(scf_model))])\n",
+    "    vals, _ = np.linalg.eigh(scf_model[next(iter(scf_model))])\n",
     "    # Extract Fermi energy.\n",
     "    E_F = utils.get_fermi_energy(vals, filling)\n",
     "    return vals - E_F"
@@ -154,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 89,
    "id": "32b9e7c5-db12-44f9-930c-21e5494404b8",
    "metadata": {
     "tags": []
@@ -176,14 +175,11 @@
     "\n",
     "    vals = []\n",
     "    for U in tqdm(Us):\n",
-    "        gap_U = []\n",
     "        vals_U = []\n",
     "        for V in Vs:\n",
     "            int_model = {(): U * onsite_int + V * nn_int}\n",
-    "            _vals = compute_vals(\n",
-    "                tb_model=tb_model,\n",
-    "                int_model=int_model,\n",
-    "            )\n",
+    "            full_model = model.Model(tb_model=tb_model, int_model=int_model)\n",
+    "            _vals = compute_vals(full_model)\n",
     "            vals_U.append(_vals)\n",
     "        vals.append(vals_U)\n",
     "    return np.asarray(vals)"
@@ -191,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 90,
    "id": "6a8c08a9-7e31-420b-b6b4-709abfb26793",
    "metadata": {
     "tags": []
@@ -201,7 +197,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 20/20 [00:17<00:00,  1.13it/s]\n"
+      "100%|██████████| 20/20 [00:05<00:00,  3.98it/s]\n"
      ]
     }
    ],
@@ -214,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 91,
    "id": "e17fc96c-c463-4e1f-8250-c254d761b92a",
    "metadata": {},
    "outputs": [],
@@ -239,13 +235,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 92,
    "id": "868cf368-45a0-465e-b042-6182ff8b6998",
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1300x300 with 5 Axes>"
       ]
@@ -262,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 93,
    "id": "0cb395cd-84d1-49b4-89dd-da7a2d09c8d0",
    "metadata": {},
    "outputs": [],
@@ -287,7 +283,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.6"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {