tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AD-NEGF hands-on\n",
    "This notebook contains the step-by-step information to use AD-NEGF to compute electronic structure and transport properties.\n",
    "\n",
    "Starting from constructing a Tight-Binding Model:\n",
    "\n",
    "first, we import the necessary packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ase\n",
    "import torch\n",
    "from ase.io import read\n",
    "from TB.hamiltonian import Hamiltonian\n",
    "from transport import Transport_tb\n",
    "from torch.autograd.functional import hessian, jacobian\n",
    "from Constant import *\n",
    "from ase.lattice.cubic import FaceCenteredCubic\n",
    "from ase.visualize.plot import plot_atoms\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "import numpy as np\n",
    "import os\n",
    "from TB.orbitals import Orbitals\n",
    "from TB.hamiltonian_initializer import set_tb_params, set_tb_params_bond_length\n",
    "\n",
    "kBT = k * T / eV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_syst(file):\n",
    "    atoms = read(file)\n",
    "    period = np.array([list(atoms.get_cell()[2])])\n",
    "    period[:, [1, 2]] = period[:, [2, 1]]\n",
    "    print(period)\n",
    "    coord = atoms.get_positions()\n",
    "\n",
    "    coord[:, [1, 2]] = coord[:, [2, 1]]\n",
    "    coords = []\n",
    "    coords.append(str(len(coord)))\n",
    "    coords.append('C')\n",
    "\n",
    "    for j, item in enumerate(coord):\n",
    "        coords.append('C' + str(j + 1) + ' ' + str(item[0]) + ' ' + str(item[1]) + ' ' + str(item[2]))\n",
    "\n",
    "    coords = '\\n'.join(coords)\n",
    "\n",
    "    return coords, period, atoms\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def radial_dependence_func(bond_length, ne_bond_length, param):\n",
    "\n",
    "        # return (bond_length / ne_bond_length) ** param\n",
    "        return torch.exp(-param*(ne_bond_length/bond_length-1))\n",
    "\n",
    "def sorting(coords, **kwargs):\n",
    "    return np.argsort(coords[:, 1], kind='mergesort')\n",
    "\n",
    "\n",
    "def plot_dispo_atoms(atoms, dispo, index):\n",
    "    new_atoms = atoms.copy()\n",
    "    new_atoms.set_positions(newpositions=new_atoms.get_positions() + dispo[:, [0, 2, 1]])\n",
    "    # plot_band(atoms=new_atoms)\n",
    "    # new_atoms.write(\"./experimental_data/traj/dispo_au_\"+str(index)+\".vasp\")\n",
    "    plot_atoms(new_atoms, show_unit_cell=2, rotation='90x,0y,270z')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Band structure of 1st nearest neightbour graphene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from TB.aux_functions import get_k_coords\n",
    "\n",
    "\n",
    "lat_const = 1.42\n",
    "a1 = 0.5 * lat_const * 3\n",
    "a2 = 0.5 * lat_const * np.sqrt(3)\n",
    "\n",
    "period = np.array([[a1, a2, 0.0],\n",
    "                   [a1, -a2, 0.0]])\n",
    "\n",
    "lat_const_rec = 2 * np.pi / (3 * np.sqrt(3) * lat_const)\n",
    "\n",
    "\n",
    "\n",
    "special_k_points = {\n",
    "    'GAMMA': [0, 0, 0],\n",
    "    'K': [lat_const_rec * np.sqrt(3), lat_const_rec, 0],\n",
    "    'K_prime': [lat_const_rec * np.sqrt(3), -lat_const_rec, 0],\n",
    "    'M': [lat_const_rec * np.sqrt(3), 0, 0]\n",
    "}\n",
    "\n",
    "sym_points = ['GAMMA', 'M', 'K', 'GAMMA']\n",
    "num_points = [25, 25, 25]\n",
    "k_points = get_k_coords(sym_points, num_points, special_k_points)\n",
    "\n",
    "fig_counter = 1\n",
    "\n",
    "def graphene_first_nearest_neighbour():\n",
    "\n",
    "    coords = \"\"\"2\n",
    "    Graphene\n",
    "    C1   0.00   0.00   0.00\n",
    "    C2   {}   0.00   0.00\n",
    "    \"\"\".format(lat_const)\n",
    "\n",
    "    s_orb = Orbitals('C')\n",
    "    s_orb.add_orbital(\"pz\", energy=-0.28, orbital=1, magnetic=0, spin=0)\n",
    "\n",
    "    # ------------------------ set TB parameters----------------------\n",
    "\n",
    "    gamma0 = -2.97\n",
    "    gamma1 = -0.073\n",
    "    gamma2 = -0.33\n",
    "    s0 = 0.073\n",
    "    s1 = 0.018\n",
    "    s2 = 0.026\n",
    "\n",
    "    set_tb_params(s_orb, PARAMS_C_C1={'pp_pi': gamma0},\n",
    "                     PARAMS_C_C2={'pp_pi': gamma1},\n",
    "                     PARAMS_C_C3={'pp_pi': gamma2},\n",
    "                     OV_C_C1={'pp_pi': s0},\n",
    "                     OV_C_C2={'pp_pi': s1},\n",
    "                     OV_C_C3={'pp_pi': s2})\n",
    "    \n",
    "\n",
    "\n",
    "    # --------------------------- Hamiltonian -------------------------\n",
    "\n",
    "    h = Hamiltonian(xyz=coords, xyz_new=coords, nn_distance=[1.6, 2.8, 3.2], comp_overlap=True,\n",
    "    sort_func=None, dtype=torch.complex128)\n",
    "    h.initialize()\n",
    "    h.set_periodic_bc(period)\n",
    "\n",
    "    \n",
    "\n",
    "    band_structure = np.zeros((sum(num_points), h.h_matrix.shape[0]))\n",
    "\n",
    "    for jj, item in enumerate(k_points):\n",
    "        band_structure[jj, :], _ = h.diagonalize_periodic_bc(item)\n",
    "\n",
    "    # visualize\n",
    "    global fig_counter\n",
    "    plt.figure(fig_counter)\n",
    "    fig_counter += 1\n",
    "    ax = plt.axes()\n",
    "    ax.set_title(r'Band structure of graphene, 3ed NN')\n",
    "    ax.set_ylabel('Energy (eV)')\n",
    "    ax.plot(np.sort(band_structure), 'k')\n",
    "    ax.plot([0, band_structure.shape[0]], [0, 0], '--', color='k', linewidth=0.5)\n",
    "    plt.xticks(np.insert(np.cumsum(num_points) - 1, 0, 0), labels=sym_points)\n",
    "    ax.xaxis.grid()\n",
    "    plt.ylim([-20.1, 25])\n",
    "    plt.show()\n",
    "\n",
    "graphene_first_nearest_neighbour()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Band structure of 1st nearest neightbour carbon nanotube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.00000000e-16 2.46680383e+00 2.00000000e-16]]\n",
      "C\n",
      "(100, 20)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from TB.aux_functions import get_k_coords\n",
    "\n",
    "\n",
    "\n",
    "fig_counter = 1\n",
    "\n",
    "def graphene_first_nearest_neighbour():\n",
    "\n",
    "    coords, period, atoms = init_syst(\"./data/CNT_medal/POSCAR\")\n",
    "    kpoints = torch.zeros(100, 3).double()\n",
    "    kpoints[:,1] = torch.linspace(0, 3.14159265/period[0][1], 100)\n",
    "\n",
    "    \n",
    "    s_orb = Orbitals('C')\n",
    "    s_orb.add_orbital(\"pz\", energy=0., orbital=1, magnetic=0, spin=0)\n",
    "\n",
    "    # ------------------------ set TB parameters----------------------\n",
    "\n",
    "    t = -3.033\n",
    "    s = 0.129\n",
    "    set_tb_params(s_orb, PARAMS_C_C={'pp_pi': t}, OV_C_C={\"pp_pi\": s})\n",
    "\n",
    "    c_c = 1.42\n",
    "    c_c_pow = 3.37\n",
    "\n",
    "    set_tb_params_bond_length(s_orb, BL_C_C={'bl': c_c, 'pp_pi': c_c_pow}\n",
    "                              )\n",
    "    \n",
    "\n",
    "\n",
    "    # --------------------------- Hamiltonian -------------------------\n",
    "\n",
    "    h = Hamiltonian(xyz=coords, xyz_new=coords, nn_distance=1.5, comp_overlap=True,\n",
    "    sort_func=None, dtype=torch.complex128, radial_dep=radial_dependence_func)\n",
    "    h.initialize()\n",
    "    h.set_periodic_bc(period)\n",
    "\n",
    "    band_structure = np.zeros((len(kpoints), h.h_matrix.shape[0]))\n",
    "    print(band_structure.shape)\n",
    "\n",
    "    for jj, item in enumerate(kpoints):\n",
    "        band_structure[jj, :], _ = h.diagonalize_periodic_bc(item)\n",
    "\n",
    "    # visualize\n",
    "    global fig_counter\n",
    "    plt.figure(fig_counter)\n",
    "    fig_counter += 1\n",
    "    ax = plt.axes()\n",
    "    ax.set_title(r'Band structure of CNT, 1ed NN')\n",
    "    ax.set_ylabel('Energy (eV)')\n",
    "    ax.plot(kpoints[:,1], np.sort(band_structure), 'k')\n",
    "    # ax.plot([0, band_structure.shape[0]], [0, 0], '--', color='k', linewidth=0.5)\n",
    "    # plt.xticks(np.insert(np.cumsum(num_points) - 1, 0, 0), labels=sym_points)\n",
    "    ax.xaxis.grid()\n",
    "    # plt.ylim([-20.1, 25])\n",
    "    plt.show()\n",
    "\n",
    "graphene_first_nearest_neighbour()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "defining the functions to compute the NEGF transmission, the following computation may takes dozen of minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTT(ul, ur, transport_tb, ee, period):\n",
    "    if isinstance(ul, (float, int)):\n",
    "        ul = torch.scalar_tensor(ul)\n",
    "    if isinstance(ur, (float, int)):\n",
    "        ur = torch.scalar_tensor(ur)\n",
    "    return transport_tb.negf.calGreen(\n",
    "        ee=ee,\n",
    "        ul=ul,\n",
    "        ur=ur,\n",
    "        atom_coord=transport_tb.h.get_site_coordinates()[transport_tb.h._offsets],\n",
    "        d_trains=1,\n",
    "        left_pos=period[0][0],\n",
    "        right_pos=period[0][1],\n",
    "        offset=transport_tb.h._offsets,\n",
    "        calDOS=False,\n",
    "        calTT=True,\n",
    "        calSeebeck=False,\n",
    "        etaLead=1e-6,\n",
    "        etaDevice=0.,\n",
    "        ifSCF=True,\n",
    "        n_int_neq=100,\n",
    "        cutoff=False,\n",
    "        sgfMethod='Lopez-Schro'\n",
    "    )['TT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.         51.12000275  0.        ]]\n",
      "C\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# ------------------------ set TB parameters----------------------\n",
    "ul = 0\n",
    "ur = 0\n",
    "ee = torch.linspace(-1.5, 1.5, 100)\n",
    "coords, period, atoms = init_syst(\"./data/C.vasp\")\n",
    "\n",
    "s_orb = Orbitals('C')\n",
    "s_orb.add_orbital(\"pz\", energy=0., orbital=1, magnetic=0, spin=0)\n",
    "# s_orb.add_orbital(\"py\", energy=0, orbital=1, magnetic=1, spin=0)\n",
    "# s_orb.add_orbital(\"px\", energy=0, orbital=1, magnetic=-1, spin=0)\n",
    "\n",
    "# ------------------------ set TB parameters----------------------\n",
    "\n",
    "t = -3.033\n",
    "s = 0.129\n",
    "set_tb_params(s_orb, PARAMS_C_C={'pp_pi': t}, OV_C_C={\"pp_pi\": s})\n",
    "\n",
    "c_c = 1.42\n",
    "c_c_pow = 3.37\n",
    "\n",
    "set_tb_params_bond_length(s_orb, BL_C_C={'bl': c_c, 'pp_pi': c_c_pow}\n",
    "                            )\n",
    "\n",
    "transport_tb = Transport_tb(\n",
    "    radial_dep=radial_dependence_func, xyz=coords, xyz_new=coords,\n",
    "    period=period, nn_distance=1.7, comp_overlap=True,\n",
    "    sort_func=sorting, dtype=torch.complex128\n",
    ")\n",
    "\n",
    "TT = getTT(ul=ul, ur=ur, transport_tb=transport_tb, ee=ee, period=period)\n",
    "\n",
    "plt.plot(ee, TT)\n",
    "plt.xlabel(\"E/eV\")\n",
    "plt.ylabel(\"T(E)\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Current of 1NN nanotube\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.         34.08000183  0.        ]]\n",
      "C\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 1/10 [00:22<03:25, 22.84s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Lopez-scheme not-so-well converged for RIGHT electrode at E = 0.0007 eV: 1.0609724490787739e-10\n",
      "WARNING: Lopez-scheme not-so-well converged for RIGHT electrode at E = -0.0003 eV: 2.2785074289256893e-10\n",
      "Converged very well at 3.\n",
      "Convergence achieved !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 2/10 [03:53<17:47, 133.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Lopez-scheme not-so-well converged for RIGHT electrode at E = 0.0007 eV: 2.071920746311495e-10\n",
      "Converged very well at 6.\n",
      "Convergence achieved !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 3/10 [08:59<24:45, 212.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged very well at 6.\n",
      "Convergence achieved !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 4/10 [14:07<24:59, 249.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged very well at 6.\n",
      "Convergence achieved !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 5/10 [20:14<24:21, 292.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged very well at 6.\n",
      "Convergence achieved !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 6/10 [26:33<21:26, 321.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged very well at 6.\n",
      "Convergence achieved !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 7/10 [33:31<17:39, 353.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged very well at 12.\n",
      "Convergence achieved !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 8/10 [43:08<14:09, 424.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged very well at 9.\n",
      "Convergence achieved !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 9/10 [52:23<07:45, 465.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Lopez-scheme not-so-well converged for RIGHT electrode at E = -1.9474 eV: 1.0809695669527346e-10\n",
      "Converged very well at 18.\n",
      "Convergence achieved !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [1:05:33<00:00, 393.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.6443e-11, 4.3972e-06, 1.1939e-05, 1.4474e-03, 8.4661e-02, 2.0374e-01,\n",
      "        3.2290e-01, 4.6359e-01, 5.9494e-01, 7.2357e-01], dtype=torch.float64)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "s_orb = Orbitals('C')\n",
    "s_orb.add_orbital(\"pz\", energy=0., orbital=1, magnetic=0, spin=0)\n",
    "# ------------------------ set TB parameters----------------------\n",
    "\n",
    "t = -3.033\n",
    "s = 0.129\n",
    "set_tb_params(s_orb, PARAMS_C_C={'pp_pi': t}, OV_C_C={\"pp_pi\": s})\n",
    "\n",
    "c_c = 1.42\n",
    "c_c_pow = 3.37\n",
    "\n",
    "set_tb_params_bond_length(s_orb, BL_C_C={'bl': c_c, 'pp_pi': c_c_pow}\n",
    "                            )\n",
    "\n",
    "\n",
    "# __________________________________ calc _________________________________\n",
    "ul = 0\n",
    "ur = 0\n",
    "ee = torch.linspace(-4.5, 4.5, 200)\n",
    "coords, period, atoms = init_syst(\"./data/CNT/SPOSCAR\")\n",
    "\n",
    "transport_tb = Transport_tb(\n",
    "    radial_dep=radial_dependence_func, xyz=coords, xyz_new=coords,\n",
    "    period=period, nn_distance=1.5, comp_overlap=True,\n",
    "    sort_func=sorting, dtype=torch.complex128\n",
    ")\n",
    "\n",
    "current = []\n",
    "for i in tqdm(range(10)):\n",
    "\n",
    "    I = transport_tb.negf.calCurrent_NUM(\n",
    "        ul=ul, \n",
    "        ur=i*0.2,\n",
    "        ifSCF=True,\n",
    "        atom_coord=transport_tb.h.get_site_coordinates()[transport_tb.h._offsets],\n",
    "        d_trains=1,\n",
    "        left_pos=period[0][0],\n",
    "        right_pos=period[0][1],\n",
    "        offset=transport_tb.h._offsets,\n",
    "        n_img=500\n",
    "        )\n",
    "    \n",
    "    current.append(I)\n",
    "\n",
    "print(torch.tensor(current))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.6443e-11, 4.3972e-06, 1.1939e-05, 1.4474e-03, 8.4661e-02, 2.0374e-01,\n",
      "        3.2290e-01, 4.6359e-01, 5.9494e-01, 7.2357e-01], dtype=torch.float64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(torch.tensor(current))\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(current)\n",
    "plt.xlabel(r\"$\\Delta$V\")\n",
    "plt.ylabel(r\"Current/$\\mu$A\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "743ed64c279c2634ef0adbd9bbb01db06157a77f8e753b7f12f47625a33470fb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}