diff --git a/examples/diatomic_molecule.ipynb b/examples/diatomic_molecule.ipynb
index 990541272b1895f671c55088e61003e9d2c878de..3f893c25048a8a03069a4502acf4b697666a15d2 100644
--- a/examples/diatomic_molecule.ipynb
+++ b/examples/diatomic_molecule.ipynb
@@ -9,10 +9,9 @@
    },
    "outputs": [],
    "source": [
-    "import kwant\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from codes import utils, hf, model\n",
+    "from codes import utils, model, interface, solvers\n",
     "from tqdm import tqdm\n",
     "from itertools import product"
    ]
@@ -98,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 10,
    "id": "41bd9f60-8f29-4e7c-a0c4-a0bbf66445b2",
    "metadata": {},
    "outputs": [],
@@ -109,15 +108,14 @@
     "):\n",
     "\n",
     "    # Run SCF loop to find groundstate Hamiltonian.\n",
-    "    scf_model = hf.find_groundstate_ham(\n",
+    "    scf_model = interface.find_groundstate_ham(\n",
     "        model,\n",
     "        filling=filling,\n",
-    "        solver=hf.finite_system_solver,\n",
-    "        cutoff_Vk=0,\n",
-    "        optimizer_kwargs={'M':2},\n",
+    "        solver=solvers.finite_system_solver,\n",
+    "        optimizer_kwargs={'w0':1e-3}\n",
     "    )\n",
     "    # Diagonalize groundstate Hamiltonian.\n",
-    "    vals, _ = np.linalg.eigh(scf_model[next(iter(scf_model))])\n",
+    "    vals, _ = np.linalg.eigh(scf_model[()])\n",
     "    # Extract Fermi energy.\n",
     "    E_F = utils.get_fermi_energy(vals, filling)\n",
     "    return vals - E_F"
@@ -153,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 11,
    "id": "32b9e7c5-db12-44f9-930c-21e5494404b8",
    "metadata": {
     "tags": []
@@ -187,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 12,
    "id": "6a8c08a9-7e31-420b-b6b4-709abfb26793",
    "metadata": {
     "tags": []
@@ -197,7 +195,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 20/20 [00:05<00:00,  3.98it/s]\n"
+      "  0%|          | 0/20 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 20/20 [00:15<00:00,  1.28it/s]\n"
      ]
     }
    ],
@@ -210,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 13,
    "id": "e17fc96c-c463-4e1f-8250-c254d761b92a",
    "metadata": {},
    "outputs": [],
@@ -235,13 +240,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 14,
    "id": "868cf368-45a0-465e-b042-6182ff8b6998",
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1300x300 with 5 Axes>"
       ]
@@ -252,13 +257,13 @@
    ],
    "source": [
     "# New result 0D\n",
-    "ds.vals.plot(col='n')\n",
+    "ds.vals.plot(x='Us', y='Vs', col='n')\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 15,
    "id": "0cb395cd-84d1-49b4-89dd-da7a2d09c8d0",
    "metadata": {},
    "outputs": [],