{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Band structure calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we will see, how to calculate band structures using pyiron. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:22.524072Z",
     "start_time": "2020-04-07T11:26:20.427068Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyiron.project import Project\n",
    "from ase.spacegroup import crystal\n",
    "import matplotlib.pyplot as plt\n",
    "import seekpath as sp\n",
    "import numpy as np\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:23.295730Z",
     "start_time": "2020-04-07T11:26:22.525484Z"
    }
   },
   "outputs": [],
   "source": [
    "pr = Project(\"band_structure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr.remove_jobs(recursive=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Structures with seekpath"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we see how [seekpath](https://github.com/giovannipizzi/seekpath) works! Therefore we create firts a structure using pyiron."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create structure with pyiron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:23.537330Z",
     "start_time": "2020-04-07T11:26:23.297431Z"
    }
   },
   "outputs": [],
   "source": [
    "structure_Fe = pr.create_structure(\"Fe\", \"bcc\", 2.81)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:23.602641Z",
     "start_time": "2020-04-07T11:26:23.538727Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c390e19106a7493cbe35773db4b83c64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_ColormakerRegistry()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98d0b747a42c4b44b0c7e9009369de27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structure_Fe.plot3d()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:23.607941Z",
     "start_time": "2020-04-07T11:26:23.603845Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fe: [0. 0. 0.]\n",
       "Fe: [1.405 1.405 1.405]\n",
       "pbc: [ True  True  True]\n",
       "cell: \n",
       "[[2.81000000e+00 0.00000000e+00 0.00000000e+00]\n",
       " [1.72062875e-16 2.81000000e+00 0.00000000e+00]\n",
       " [1.72062875e-16 1.72062875e-16 2.81000000e+00]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_Fe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create structure with seekpath"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For seekpath we need a tuple containing\n",
    "1. The cell in $3\\times3$ array\n",
    "2. The scaled positions\n",
    "3. List of `ints` to distinguish the atom types (indices of pyiron structure)\n",
    "as input structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:23.615746Z",
     "start_time": "2020-04-07T11:26:23.609164Z"
    }
   },
   "outputs": [],
   "source": [
    "input_sp = (structure_Fe.cell, structure_Fe.get_scaled_positions(), structure_Fe.indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to see how the output looks like, let us do..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:23.638030Z",
     "start_time": "2020-04-07T11:26:23.617041Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'point_coords': {'GAMMA': [0.0, 0.0, 0.0],\n",
       "  'H': [0.5, -0.5, 0.5],\n",
       "  'P': [0.25, 0.25, 0.25],\n",
       "  'N': [0.0, 0.0, 0.5]},\n",
       " 'path': [('GAMMA', 'H'),\n",
       "  ('H', 'N'),\n",
       "  ('N', 'GAMMA'),\n",
       "  ('GAMMA', 'P'),\n",
       "  ('P', 'H'),\n",
       "  ('P', 'N')],\n",
       " 'has_inversion_symmetry': True,\n",
       " 'augmented_path': False,\n",
       " 'bravais_lattice': 'cI',\n",
       " 'bravais_lattice_extended': 'cI1',\n",
       " 'conv_lattice': array([[2.81, 0.  , 0.  ],\n",
       "        [0.  , 2.81, 0.  ],\n",
       "        [0.  , 0.  , 2.81]]),\n",
       " 'conv_positions': array([[0. , 0. , 0. ],\n",
       "        [0.5, 0.5, 0.5]]),\n",
       " 'conv_types': array([0, 0], dtype=int32),\n",
       " 'primitive_lattice': array([[-1.405,  1.405,  1.405],\n",
       "        [ 1.405, -1.405,  1.405],\n",
       "        [ 1.405,  1.405, -1.405]]),\n",
       " 'primitive_positions': array([[0., 0., 0.]]),\n",
       " 'primitive_types': array([0], dtype=int32),\n",
       " 'reciprocal_primitive_lattice': [[-0.0, 2.236009006113732, 2.236009006113732],\n",
       "  [2.236009006113732, 0.0, 2.236009006113732],\n",
       "  [2.236009006113732, 2.236009006113732, 0.0]],\n",
       " 'inverse_primitive_transformation_matrix': array([[0, 1, 1],\n",
       "        [1, 0, 1],\n",
       "        [1, 1, 0]]),\n",
       " 'primitive_transformation_matrix': array([[-0.5,  0.5,  0.5],\n",
       "        [ 0.5, -0.5,  0.5],\n",
       "        [ 0.5,  0.5, -0.5]]),\n",
       " 'volume_original_wrt_conv': 0.9999999999999999,\n",
       " 'volume_original_wrt_prim': 1.9999999999999998,\n",
       " 'spacegroup_number': 229,\n",
       " 'spacegroup_international': 'Im-3m'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.get_path(input_sp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code creates automatically the conventional and primitive cell with all high-symmetry points and a suggested path taking all high-symmetry paths into account.\n",
    "\n",
    "**Keep in mind:** The high-symmetry points and paths make only sence for the primitive cell! Therefore we run all calculations in the primitive cell created by seekpath."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the same structure as before!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create new structure (primitive cell) with high-symmetry points and paths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the following command all arguments valid for seekpath are supported. Look at the docstring and at seekpath."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "structure_Fe_sp = structure_Fe.create_line_mode_structure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "944b54809a6944718312d68a8d649bb0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structure_Fe_sp.plot3d()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fe: [0. 0. 0.]\n",
       "pbc: [ True  True  True]\n",
       "cell: \n",
       "[[-1.405  1.405  1.405]\n",
       " [ 1.405 -1.405  1.405]\n",
       " [ 1.405  1.405 -1.405]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_Fe_sp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see, that the structure is now the primitive cell containing only one atom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GAMMA': [0.0, 0.0, 0.0],\n",
       " 'H': [0.5, -0.5, 0.5],\n",
       " 'P': [0.25, 0.25, 0.25],\n",
       " 'N': [0.0, 0.0, 0.5]}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_Fe_sp.get_high_symmetry_points()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'full': [('GAMMA', 'H'),\n",
       "  ('H', 'N'),\n",
       "  ('N', 'GAMMA'),\n",
       "  ('GAMMA', 'P'),\n",
       "  ('P', 'H'),\n",
       "  ('P', 'N')]}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_Fe_sp.get_high_symmetry_path()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The path is stored like this. Here you can also add paths to the dictionary.\n",
    "\n",
    "Each tuple gives a start and end point for this specific trace. Thus also disonnected paths are possible to calculate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "structure_Fe_sp.add_high_symmetry_path({\"my_path\": [(\"GAMMA\", \"H\"), (\"GAMMA\", \"P\")]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'full': [('GAMMA', 'H'),\n",
       "  ('H', 'N'),\n",
       "  ('N', 'GAMMA'),\n",
       "  ('GAMMA', 'P'),\n",
       "  ('P', 'H'),\n",
       "  ('P', 'N')],\n",
       " 'my_path': [('GAMMA', 'H'), ('GAMMA', 'P')]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure_Fe_sp.get_high_symmetry_path()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create jobs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need two jobs for a band structure! The first gives us the correct Fermi energy and the charge densities used for the second calculations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create job for charge density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is only a small example for BS calculations. Could be that the input parameter like cutoff etc. does not make much sense... for real physics..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:28.382332Z",
     "start_time": "2020-04-07T11:26:28.377845Z"
    }
   },
   "outputs": [],
   "source": [
    "def setup_hamiltonian_sphinx(project, jobname, structure, chgcar_file=\"\"): \n",
    "    \n",
    "    #version 1.0 (08.03.2019)\n",
    "    \n",
    "    #Name und typ\n",
    "    ham = project.create_job(job_type='Sphinx', job_name=jobname)\n",
    "    \n",
    "    #parameter für xc functional\n",
    "    ham.exchange_correlation_functional = 'PBE'\n",
    "    \n",
    "    #struktur\n",
    "    ham.structure = structure\n",
    "    \n",
    "    ham.set_encut(450)\n",
    "    ham.set_empty_states(6)\n",
    "    ham.set_convergence_precision(electronic_energy=1e-8)\n",
    "    ham.set_occupancy_smearing(width=0.2)\n",
    "          \n",
    "    #parameter für kpoints\n",
    "    ham.set_kpoints([8, 8, 8])\n",
    "    \n",
    "    return ham"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:28.778390Z",
     "start_time": "2020-04-07T11:26:28.383459Z"
    }
   },
   "outputs": [],
   "source": [
    "ham_spx_chg = setup_hamiltonian_sphinx(pr, \"Fe_spx_CHG\", structure_Fe_sp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:29.025579Z",
     "start_time": "2020-04-07T11:26:28.823298Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Fe_spx_CHG was saved and received the ID: 1\n"
     ]
    }
   ],
   "source": [
    "ham_spx_chg.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:29.251302Z",
     "start_time": "2020-04-07T11:26:29.026672Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "finished    1\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.get_jobs_status()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create second job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We restart the fist job with the following command. Then the charge density of the first job is taken for the second!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:30.064390Z",
     "start_time": "2020-04-07T11:26:29.252379Z"
    }
   },
   "outputs": [],
   "source": [
    "ham_spx_bs = ham_spx_chg.restart_for_band_structure_calculations(job_name=\"Fe_spx_BS\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ham_spx_bs = ham_spx_chg.restart_from_charge_density(\n",
    "             job_name=\"Fe_spx_BS\",\n",
    "             job_type=None,\n",
    "             band_structure_calc=True\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set line mode for k-points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To set the correct path, we have to give the name of the path (in our example either `full` or `my_path`) and the number of points for each subpath (would be for `n_path=100`and `path_name=\"my_path\"` 200 k-points in total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:30.109050Z",
     "start_time": "2020-04-07T11:26:30.106507Z"
    }
   },
   "outputs": [],
   "source": [
    "ham_spx_bs.set_kpoints(scheme=\"Line\", path_name=\"full\", n_path=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A parameter usefull for BS calculations. Look at the sphinx manual for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:30.116670Z",
     "start_time": "2020-04-07T11:26:30.110054Z"
    }
   },
   "outputs": [],
   "source": [
    "ham_spx_bs.input[\"nSloppy\"] = 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:30.330868Z",
     "start_time": "2020-04-07T11:26:30.117645Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job Fe_spx_BS was saved and received the ID: 2\n"
     ]
    }
   ],
   "source": [
    "ham_spx_bs.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:30.553836Z",
     "start_time": "2020-04-07T11:26:30.331946Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "finished    2\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.get_jobs_status()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Store the data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The energy values are stored in the following paths of the hdf5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:30.596444Z",
     "start_time": "2020-04-07T11:26:30.554860Z"
    }
   },
   "outputs": [],
   "source": [
    "energy_sphinx = ham_spx_bs['output/generic/dft/bands_eigen_values'][-1]\n",
    "ef_sphinx = ham_spx_chg['output/generic/dft/bands_e_fermi'][-1]\n",
    "energy_sphinx -= ef_sphinx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can easily plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-07T11:26:30.739721Z",
     "start_time": "2020-04-07T11:26:30.597477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 252,
       "width": 386
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(energy_sphinx[:-1], 'b-')\n",
    "plt.axhline(y=0, ls='--', c='k')\n",
    "plt.xlim(0, len(energy_sphinx));\n",
    "plt.ylim(-10,40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}