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z9_SjCqemka4ehP5Jt`ept{B<R%8{H}G)A=}uQv>T`?3Fy@B8rXdZHgA?{6C3oF)22 g?H3Xrf9uHlZE$dKM5ujaJMecJ|ML+39~<WX0nNfW)Bpeg diff --git a/examples/diatomic_molecule.ipynb b/examples/diatomic_molecule.ipynb index 81f1b95..4b6a18f 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": { - "image/png": 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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": [], -- GitLab