diff --git a/examples/data/diatomic_molecule_example.nc b/examples/data/diatomic_molecule_example.nc
index b400786ecadaed279b1db5d59b1acb57431880a0..72606ebef5db3c368507c337ae52eae4c014103b 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 81f1b9595c777a9718fce9fafe7e36326d242bad..4b6a18f515a72166e1e4361160db4829ccfff1f1 100644
--- a/examples/diatomic_molecule.ipynb
+++ b/examples/diatomic_molecule.ipynb
@@ -44,18 +44,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 2,
    "id": "d31cbfea-18ea-454e-8a63-d706a85cd3fc",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Just writing the Hamiltonian above in numpy\n",
-    "hamiltonian_0 = np.block([\n",
-    "    [0 * np.eye(2), np.eye(2)],\n",
-    "    [np.eye(2), 0 * np.eye(2)]\n",
-    "])\n",
-    "# Here we add a dummy index because that is interpreted as a Γ-point calculation.\n",
-    "hamiltonian_0 = np.expand_dims(hamiltonian_0, axis=0)"
+    "# Here we add a dummy index to make the notation compatible with infinite systems.\n",
+    "tb_model = {(): np.kron(np.array([[0, 1], [1, 0]]), np.eye(2))}"
    ]
   },
   {
@@ -68,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 3,
    "id": "b39a2976-7c35-4670-83ef-12157bd3fc0e",
    "metadata": {},
    "outputs": [
@@ -84,8 +80,9 @@
     }
    ],
    "source": [
-    "vals, vecs = np.linalg.eigh(hamiltonian_0[0])\n",
-    "plt.plot(vals, 'o')\n",
+    "hamiltonian_0 = tb_model[next(iter(tb_model))]\n",
+    "vals, vecs = np.linalg.eigh(hamiltonian_0)\n",
+    "plt.plot(vals, \"o\")\n",
     "plt.show()"
    ]
   },
@@ -95,42 +92,33 @@
    "metadata": {},
    "source": [
     "We now move to an eigenvalue calculation of the Hartree-Fock solution. The workflow is rather simple:\n",
-    "* Generate a random guess.\n",
     "* Run the self-consistent loop.\n",
     "* Diagonalize the mean-field Hamiltonian."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 4,
    "id": "41bd9f60-8f29-4e7c-a0c4-a0bbf66445b2",
    "metadata": {},
    "outputs": [],
    "source": [
     "def compute_vals(\n",
-    "    H_int,\n",
-    "    hamiltonian_0=hamiltonian_0,\n",
+    "    tb_model,\n",
+    "    int_model,\n",
     "    filling=2,\n",
-    "    tol=1e-5,\n",
-    "    mixing=0.01,\n",
-    "    order=10,\n",
-    "    guess=None\n",
     "):\n",
-    "    # Generate random guess with same shape as the Hamiltonian.\n",
-    "    guess = np.random.rand(*hamiltonian_0.shape) * np.exp(1j * 2 * np.pi * np.random.rand(*hamiltonian_0.shape))\n",
     "\n",
     "    # Run SCF loop to find groundstate Hamiltonian.\n",
-    "    h = hf.find_groundstate_ham(\n",
-    "        H_int=H_int,\n",
+    "    scf_model = hf.find_groundstate_ham(\n",
+    "        tb_model=tb_model,\n",
+    "        int_model=int_model,\n",
     "        filling=filling,\n",
-    "        hamiltonians_0=hamiltonian_0,\n",
-    "        tol=tol,\n",
-    "        guess=guess,\n",
-    "        mixing=mixing,\n",
-    "        order=order,\n",
+    "        tol=1e-4,\n",
+    "        nk=2,\n",
     "    )\n",
     "    # Diagonalize groundstate Hamiltonian.\n",
-    "    vals, vecs = np.linalg.eigh(h)\n",
+    "    vals, vecs = 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"
@@ -148,7 +136,7 @@
     "\\end{align}\n",
     "where from the first to the second line we removed the terms that are not allowed by the exclusion principle. These are however taken care of by the algorithm, so we in fact just need to provide $U_i$ and $V_{ij}$. We simplify the Hamiltonian further as:\n",
     "\\begin{align}\n",
-    "H_{int} = U \\sum_i n_{i\\uparrow} n_{i\\downarrow} + V_{ij} \\sum_{\\langle i, j \\rangle} n_i n_j~.\n",
+    "H_{int} = U \\sum_i n_{i\\uparrow} n_{i\\downarrow} + V \\sum_{\\langle i, j \\rangle} n_i n_j~.\n",
     "\\end{align}\n",
     "Thus, the we just need to pass to the algorithm the matrix\n",
     "$$\n",
@@ -166,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 5,
    "id": "32b9e7c5-db12-44f9-930c-21e5494404b8",
    "metadata": {
     "tags": []
@@ -176,31 +164,25 @@
     "def compute_phase_diagram(\n",
     "    Us,\n",
     "    Vs,\n",
-    "    tol=1e-5,\n",
-    "    mixing=0.1,\n",
-    "    order=5,\n",
     "):\n",
+    "    _block = np.ones((2, 2))\n",
     "    # onsite interactions\n",
-    "    onsite_int = np.block(\n",
-    "        [[np.ones((2, 2)), np.zeros((2, 2))], [np.zeros((2, 2)), np.ones((2, 2))]]\n",
-    "    )\n",
-    "    onsite_int = np.expand_dims(onsite_int, axis=0)\n",
+    "    onsite_int = np.kron(np.eye(2), _block)\n",
     "    # Nearest-neighbor interactions\n",
-    "    nn_int = np.block(\n",
-    "        [[np.zeros((2, 2)), np.ones((2, 2))], [np.zeros((2, 2)), np.zeros((2, 2))]]\n",
+    "    nn_int = np.kron(\n",
+    "        np.array([[0, 1], [1, 0]]),\n",
+    "        _block\n",
     "    )\n",
-    "    nn_int = np.expand_dims(nn_int, axis=0)\n",
+    "\n",
     "    vals = []\n",
     "    for U in tqdm(Us):\n",
     "        gap_U = []\n",
     "        vals_U = []\n",
     "        for V in Vs:\n",
-    "            H_int = U * onsite_int + V * nn_int\n",
+    "            int_model = {(): U * onsite_int + V * nn_int}\n",
     "            _vals = compute_vals(\n",
-    "                H_int=H_int,\n",
-    "                tol=tol,\n",
-    "                mixing=mixing,\n",
-    "                order=order,\n",
+    "                tb_model=tb_model,\n",
+    "                int_model=int_model,\n",
     "            )\n",
     "            vals_U.append(_vals)\n",
     "        vals.append(vals_U)\n",
@@ -209,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "6a8c08a9-7e31-420b-b6b4-709abfb26793",
    "metadata": {
     "tags": []
@@ -219,7 +201,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 65%|██████▌   | 13/20 [00:34<00:32,  4.62s/it]"
+      "100%|██████████| 20/20 [00:17<00:00,  1.13it/s]\n"
      ]
     }
    ],
@@ -227,12 +209,12 @@
     "# Interaction strengths\n",
     "Us = np.linspace(0, 5, 20, endpoint=True)\n",
     "Vs = np.linspace(0, 1, 20, endpoint=True)\n",
-    "vals = compute_phase_diagram(Us, Vs, tol=1e-5)"
+    "vals = compute_phase_diagram(Us, Vs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 7,
    "id": "e17fc96c-c463-4e1f-8250-c254d761b92a",
    "metadata": {},
    "outputs": [],
@@ -241,21 +223,29 @@
     "\n",
     "ds = xr.Dataset(\n",
     "    data_vars=dict(\n",
-    "        vals=([\"Us\", \"Vs\", \"n\"], vals[:,:,0,:]),\n",
+    "        vals=([\"Us\", \"Vs\", \"n\"], vals),\n",
     "    ),\n",
     "    coords=dict(Us=Us, Vs=Vs, n=np.arange(vals.shape[-1])),\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "070bc196-64c3-4040-9bb5-e9a216763eea",
+   "metadata": {},
+   "source": [
+    "We can now inspect how the eigenenergies evolve as a function of the interaction strength."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 8,
    "id": "868cf368-45a0-465e-b042-6182ff8b6998",
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1300x300 with 5 Axes>"
       ]
@@ -265,13 +255,14 @@
     }
    ],
    "source": [
+    "# New result 0D\n",
     "ds.vals.plot(col='n')\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 9,
    "id": "0cb395cd-84d1-49b4-89dd-da7a2d09c8d0",
    "metadata": {},
    "outputs": [],