{
 "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": "d8fb2ad12c684f5aacccfdaac127f3bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_ColormakerRegistry()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05710635034d4a22a085dc65e24c404f",
       "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': [[1.6776982741209693e-16,\n",
       "   2.236009006113732,\n",
       "   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": "8cfca8be8cc240c689ba45ffb07ebeb2",
       "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",
    "    ham.load_default_groups()\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: 4\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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/pyiron/base/generic/parameters.py:285: UserWarning: The input in GenericParameters changed, while the state of the job was already finished.\n",
      "  \"The input in GenericParameters changed, while the state of the job was already finished.\"\n"
     ]
    }
   ],
   "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: 5\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",
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