notebooks/energy_volume_curve.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Energy volume curve\n",
    "## Theory \n",
    "Fitting the energy volume curve allows to calculate the equilibrium energy $E_0$, the equilirbium volume $V_0$, the equilibrium bulk modulus $B_0$ and its derivative $B^{'}_0$. These quantities can then be used as part of the Einstein model to get an initial prediction for the thermodynamik properties, the heat capacity $C_v$ and the free energy $F$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing matplotlib, numpy and the pyiron project class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:08.037567Z",
     "start_time": "2019-09-04T12:58:06.149845Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pyiron import Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we create a project, by specifying the name of the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:09.049344Z",
     "start_time": "2019-09-04T12:58:08.040033Z"
    }
   },
   "outputs": [],
   "source": [
    "pr = Project(path='thermo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Atomistic structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyse the energy volume dependence a single super cell is sufficient, so we create an iron super cell as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:09.222395Z",
     "start_time": "2019-09-04T12:58:09.051675Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d12d19b951364c45ad4b3f42533841f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d399da4a95a4e03a8c3ae1ecef19444",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basis = pr.create_structure(element='Fe', bravais_basis='bcc', lattice_constant=2.75)\n",
    "basis.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation\n",
    "Energy volume curves are commonly calculated with ab initio codes, so we use VASP in this example. But we focus on the generic commands so the same example works with any DFT code. We choose 'vasp' as job name prefix, select an energy cut off of $320 eV$ and assign the basis to the job. Afterwards we apply the corresponding strain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:12.063333Z",
     "start_time": "2019-09-04T12:58:09.224176Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job gpaw_0_95 was saved and received the ID: 1\n",
      "The job gpaw_0_9666666666666667 was saved and received the ID: 2\n",
      "The job gpaw_0_9833333333333333 was saved and received the ID: 3\n",
      "The job gpaw_1_0 was saved and received the ID: 4\n",
      "The job gpaw_1_0166666666666666 was saved and received the ID: 5\n",
      "The job gpaw_1_0333333333333334 was saved and received the ID: 6\n",
      "The job gpaw_1_05 was saved and received the ID: 7\n"
     ]
    }
   ],
   "source": [
    "for strain in np.linspace(0.95, 1.05, 7):\n",
    "    strain_str = str(strain).replace('.', '_')\n",
    "    job_vasp_strain = pr.create_job(job_type=pr.job_type.Gpaw, job_name='gpaw_' + strain_str)\n",
    "    job_vasp_strain.set_encut(320.0)\n",
    "    job_vasp_strain.structure = basis.copy()\n",
    "    job_vasp_strain.structure.set_cell(cell=basis.cell * strain ** (1/3), scale_atoms=True)\n",
    "    job_vasp_strain.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-16T18:51:35.690791Z",
     "start_time": "2018-10-16T18:51:11.248Z"
    }
   },
   "source": [
    "As these are simple calculation, there is no need to submit them to the queuing sytem. We can confirm the status of the calculation with the job_table. If the status of each job is marked as finished, then we can continue with the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:12.728534Z",
     "start_time": "2019-09-04T12:58:12.118542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_95</td>\n",
       "      <td>/gpaw_0_95</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:24:24.200176</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9666666666666667</td>\n",
       "      <td>/gpaw_0_9666666666666667</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:26:45.417210</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9833333333333333</td>\n",
       "      <td>/gpaw_0_9833333333333333</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:28:37.112334</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0</td>\n",
       "      <td>/gpaw_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:30:26.714705</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0166666666666666</td>\n",
       "      <td>/gpaw_1_0166666666666666</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:31:58.800251</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0333333333333334</td>\n",
       "      <td>/gpaw_1_0333333333333334</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:34:47.304029</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_05</td>\n",
       "      <td>/gpaw_1_05</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:36:23.322563</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    status chemicalformula                      job  \\\n",
       "0   1  finished            None                gpaw_0_95   \n",
       "1   2  finished            None  gpaw_0_9666666666666667   \n",
       "2   3  finished            None  gpaw_0_9833333333333333   \n",
       "3   4  finished            None                 gpaw_1_0   \n",
       "4   5  finished            None  gpaw_1_0166666666666666   \n",
       "5   6  finished            None  gpaw_1_0333333333333334   \n",
       "6   7  finished            None                gpaw_1_05   \n",
       "\n",
       "                     subjob projectpath               project  \\\n",
       "0                /gpaw_0_95        None  /home/jovyan/thermo/   \n",
       "1  /gpaw_0_9666666666666667        None  /home/jovyan/thermo/   \n",
       "2  /gpaw_0_9833333333333333        None  /home/jovyan/thermo/   \n",
       "3                 /gpaw_1_0        None  /home/jovyan/thermo/   \n",
       "4  /gpaw_1_0166666666666666        None  /home/jovyan/thermo/   \n",
       "5  /gpaw_1_0333333333333334        None  /home/jovyan/thermo/   \n",
       "6                /gpaw_1_05        None  /home/jovyan/thermo/   \n",
       "\n",
       "                   timestart timestop totalcputime  \\\n",
       "0 2020-10-02 17:24:24.200176     None         None   \n",
       "1 2020-10-02 17:26:45.417210     None         None   \n",
       "2 2020-10-02 17:28:37.112334     None         None   \n",
       "3 2020-10-02 17:30:26.714705     None         None   \n",
       "4 2020-10-02 17:31:58.800251     None         None   \n",
       "5 2020-10-02 17:34:47.304029     None         None   \n",
       "6 2020-10-02 17:36:23.322563     None         None   \n",
       "\n",
       "                                      computer hamilton hamversion parentid  \\\n",
       "0  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "1  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "2  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "3  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "4  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "5  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "6  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "\n",
       "  masterid  \n",
       "0     None  \n",
       "1     None  \n",
       "2     None  \n",
       "3     None  \n",
       "4     None  \n",
       "5     None  \n",
       "6     None  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.job_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aggregate the data for further processing in two separated lists, one for the volumes and one for the energies. To do so we iterate over the jobs within the project, filter the job names which contain the string 'vasp' and from those extract the final volume and the final energy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:14.448207Z",
     "start_time": "2019-09-04T12:58:12.730038Z"
    }
   },
   "outputs": [],
   "source": [
    "volume_lst, energy_lst = zip(*[[job['output/generic/volume'][-1], job['output/generic/energy_pot'][-1]] \n",
    "                               for job in pr.iter_jobs(convert_to_object=False) if 'gpaw' in job.job_name])   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-16T19:02:33.916818Z",
     "start_time": "2018-10-16T19:02:33.912978Z"
    }
   },
   "source": [
    "We plot the aggregated data using matplotlib. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:14.873088Z",
     "start_time": "2019-09-04T12:58:14.449911Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Energy (eV)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(volume_lst, energy_lst, 'x-')\n",
    "plt.xlabel('Volume ($\\AA ^ 3$)')\n",
    "plt.ylabel('Energy (eV)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-16T19:12:50.900916Z",
     "start_time": "2018-10-16T19:12:50.896333Z"
    }
   },
   "source": [
    "## Encut Dependence\n",
    "To extend the complexity of our simulation protocol we can not only iterate over different strains but also different energy cutoffs. For this we use multiple sub projects to structure the data. And we summarize the previous code in multiple functions to maintain a high level of readability. The first function calculates a specific strained configuration for an specifc energy cut off, while the second function analyses the different strained calculations for a specific energy cutoff and returns the list of energy volume pairs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:14.881066Z",
     "start_time": "2019-09-04T12:58:14.876094Z"
    }
   },
   "outputs": [],
   "source": [
    "def vasp_calculation_for_strain(pr, basis, strain, encut):\n",
    "    strain_str = str(strain).replace('.', '_')\n",
    "    job_vasp_strain = pr.create_job(job_type=pr.job_type.Gpaw, job_name='gpaw_' + strain_str)\n",
    "    job_vasp_strain.set_encut(encut)\n",
    "    job_vasp_strain.structure = basis.copy()\n",
    "    job_vasp_strain.structure.set_cell(cell=basis.cell * strain ** (1/3), scale_atoms=True)\n",
    "    job_vasp_strain.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:14.895675Z",
     "start_time": "2019-09-04T12:58:14.883541Z"
    }
   },
   "outputs": [],
   "source": [
    "def energy_volume_pairs(pr):\n",
    "    volume_lst, energy_lst = zip(*[[job['output/generic/volume'][-1], job['output/generic/energy_pot'][-1]] \n",
    "                               for job in pr.iter_jobs(convert_to_object=False) if 'gpaw' in job.job_name])\n",
    "    return volume_lst, energy_lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these functions we can structure our code and implement the additional for loop to include multiple energy cutoffs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:32.618120Z",
     "start_time": "2019-09-04T12:58:14.897403Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job gpaw_0_95 was saved and received the ID: 8\n",
      "The job gpaw_0_9666666666666667 was saved and received the ID: 9\n",
      "The job gpaw_0_9833333333333333 was saved and received the ID: 10\n",
      "The job gpaw_1_0 was saved and received the ID: 11\n",
      "The job gpaw_1_0166666666666666 was saved and received the ID: 12\n",
      "The job gpaw_1_0333333333333334 was saved and received the ID: 13\n",
      "The job gpaw_1_05 was saved and received the ID: 14\n",
      "The job gpaw_0_95 was saved and received the ID: 15\n",
      "The job gpaw_0_9666666666666667 was saved and received the ID: 16\n",
      "The job gpaw_0_9833333333333333 was saved and received the ID: 17\n",
      "The job gpaw_1_0 was saved and received the ID: 18\n",
      "The job gpaw_1_0166666666666666 was saved and received the ID: 19\n",
      "The job gpaw_1_0333333333333334 was saved and received the ID: 20\n",
      "The job gpaw_1_05 was saved and received the ID: 21\n",
      "The job gpaw_0_95 was saved and received the ID: 22\n",
      "The job gpaw_0_9666666666666667 was saved and received the ID: 23\n",
      "The job gpaw_0_9833333333333333 was saved and received the ID: 24\n",
      "The job gpaw_1_0 was saved and received the ID: 25\n",
      "The job gpaw_1_0166666666666666 was saved and received the ID: 26\n",
      "The job gpaw_1_0333333333333334 was saved and received the ID: 27\n",
      "The job gpaw_1_05 was saved and received the ID: 28\n",
      "The job gpaw_0_95 was saved and received the ID: 29\n",
      "The job gpaw_0_9666666666666667 was saved and received the ID: 30\n",
      "The job gpaw_0_9833333333333333 was saved and received the ID: 31\n",
      "The job gpaw_1_0 was saved and received the ID: 32\n",
      "The job gpaw_1_0166666666666666 was saved and received the ID: 33\n",
      "The job gpaw_1_0333333333333334 was saved and received the ID: 34\n",
      "The job gpaw_1_05 was saved and received the ID: 35\n",
      "The job gpaw_0_95 was saved and received the ID: 36\n",
      "The job gpaw_0_9666666666666667 was saved and received the ID: 37\n",
      "The job gpaw_0_9833333333333333 was saved and received the ID: 38\n",
      "The job gpaw_1_0 was saved and received the ID: 39\n",
      "The job gpaw_1_0166666666666666 was saved and received the ID: 40\n",
      "The job gpaw_1_0333333333333334 was saved and received the ID: 41\n",
      "The job gpaw_1_05 was saved and received the ID: 42\n",
      "The job gpaw_0_95 was saved and received the ID: 43\n",
      "The job gpaw_0_9666666666666667 was saved and received the ID: 44\n",
      "The job gpaw_0_9833333333333333 was saved and received the ID: 45\n",
      "The job gpaw_1_0 was saved and received the ID: 46\n",
      "The job gpaw_1_0166666666666666 was saved and received the ID: 47\n",
      "The job gpaw_1_0333333333333334 was saved and received the ID: 48\n",
      "The job gpaw_1_05 was saved and received the ID: 49\n"
     ]
    }
   ],
   "source": [
    "for encut in np.linspace(270, 320, 6):\n",
    "    encut_str = 'encut_' + str(int(encut))\n",
    "    pr_encut = pr.open(encut_str)\n",
    "    for strain in np.linspace(0.95, 1.05, 7):\n",
    "        vasp_calculation_for_strain(pr=pr_encut, \n",
    "                                    basis=basis, \n",
    "                                    strain=strain, \n",
    "                                    encut=encut)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-16T19:27:02.877244Z",
     "start_time": "2018-10-16T19:27:02.873384Z"
    }
   },
   "source": [
    "The analysis is structured in a similar way. Here we use iter_groups() to iterate over the existing subprojects within our project and plot the individual energy volume curves using the functions defined above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.013569Z",
     "start_time": "2019-09-04T12:58:32.663233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f638828d6d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for pr_encut in pr.iter_groups():\n",
    "    volume_lst, energy_lst = energy_volume_pairs(pr_encut)\n",
    "    plt.plot(volume_lst, energy_lst, 'x-', label=pr_encut.base_name)\n",
    "plt.xlabel('Volume ($\\AA ^ 3$)')\n",
    "plt.ylabel('Energy (eV)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting\n",
    "After we created multiple datasets we can now start to fit the converged results. While it is possible to fit the results using a simple polynomial fit we prefer to use the phyiscally motivated birch murnaghan equation or the vinet equation. For this we create the Murnaghan object and use it is fitting functionality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.264136Z",
     "start_time": "2019-09-04T12:58:37.015213Z"
    }
   },
   "outputs": [],
   "source": [
    "murn = pr.create_job(job_type=pr.job_type.Murnaghan, job_name='murn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Birch Marnaghan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.310070Z",
     "start_time": "2019-09-04T12:58:37.302889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-16.623387215037408, 280.9875784436634, 4.060730693834813, 21.19199044120968]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[e0, b0, bP, v0], [e0_error, b0_error, bP_error, v0_error] = murn._fit_leastsq(volume_lst=volume_lst,\n",
    "                                                                               energy_lst=energy_lst,\n",
    "                                                                               fittype='birchmurnaghan')\n",
    "[e0, b0, bP, v0] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vinet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.322375Z",
     "start_time": "2019-09-04T12:58:37.311758Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-16.623384845899427, 280.93805771100557, 4.105492272090299, 21.19185363600345]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[e0, b0, bP, v0], [e0_error, b0_error, bP_error, v0_error] = murn._fit_leastsq(volume_lst=volume_lst,\n",
    "                                                                               energy_lst=energy_lst,\n",
    "                                                                               fittype='vinet')\n",
    "[e0, b0, bP, v0] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that both equation of states give slightly different results, with overall good agreement. To validate the agreement we plot the with with the original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.530517Z",
     "start_time": "2019-09-04T12:58:37.324021Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f638078d150>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAELCAYAAAAVwss1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAA1aElEQVR4nO3dd3hUZfr/8fedXgiQQOgd6R0iggiIFLEiKiuW/a67q4htLYvorg3rWgBdCyAqihVdFKUJSBcUIfRA6DUEQqhJCOn3748Z/EVMGWCSM0nu13WdazLnnGfmnsOQT55TniOqijHGGONNfk4XYIwxpvyxcDHGGON1Fi7GGGO8zsLFGGOM11m4GGOM8boApwvwBdWrV9dGjRo5XYYxxpQpq1evPqKq0QUts3ABGjVqRGxsrNNlGGNMmSIiewtbZrvFjDHGeJ2FizHGGK+zcDHGGON1dszFGGPOUXZ2NgkJCWRkZDhdSqkICQmhXr16BAYGetzGwsUYY85RQkICERERNGrUCBFxupwSpaocPXqUhIQEGjdu7HE72y1mjDHnKCMjg2rVqpX7YAEQEapVq3bOvTQLF2OMOQ8VIVjOOJ/PauFyATT1EPs+f4jcU8edLsUYY3yKhcsFWBO/g3rbJrP5m5ecLsUYU4GNGjWK0aNHs2XLFjp27EinTp3YuXMnX3zxhWM1WbhcgM4X92BFaC+a7vqEE8mJTpdjjKngvvvuOwYNGsTatWvZv3+/hUtZJSLUvuE5gjWLDV8/73Q5xpgK5KWXXqJFixb069ePrVu3kp6ezptvvskHH3xAnz59eOKJJ/jpp5/o2LEjb7zxRqnXZ6ciX6DGLTuxvtpAuh6eSvy2R2nVvLnTJRljStFzMzaxOTHFq6/Zuk5lnr2uTaHLV69ezZQpU1i7di05OTl07tyZLl26MHz4cCpVqsSIESNYvHgxo0ePZubMmV6tzVPWc/GCJkOeJ0By2TXtBfLy1OlyjDHl3E8//cTgwYMJCwujcuXKXH/99U6X9AfWc/GCiNrN2dXgRvrt/Y4ffo7lmssudrokY0wpKaqHUZJ8/VRo67l4SaPBz+InkLngVVIysp0uxxhTjvXq1Ytp06Zx+vRpUlNTmTFjxh/WiYiIIDU11YHqXCxcvMQvsgEnW9/BdXkLmTxrsdPlGGPKsc6dO3PLLbfQsWNHbrrpJnr27PmHddq3b09AQAAdOnRw5IC+qNoxgpiYGPXKzcJSD5E9th3Tc7rR7oEvaF4z4sJf0xjjc+Lj42nVqpXTZZSqgj6ziKxW1ZiC1reeizdF1CKny13c4P8T730zBwtuY0xFZeHiZaF9/kmefwi9D37I7I2HnC7HGGMcYeHibeHV8e92L9f6r+DLGT+QnpXjdEXGGFPqLFxKgF+PB8kLrMQdGV8wbtFOp8sxxphSZ+FSEsKiCOjxIAP9V/HLTz+y58gppysyxphSZeFSUrrfR15oNR7z/5IXZmxyuhpjjClVFi4lJTgCv94j6SZxZG5fyIL4JKcrMsaUY4mJidx8883n3f7NN98kPT3da/VYuJSkmL+iVRrwTMjXvDAjjozsXKcrMsaUU3Xq1GHq1Knn3d7CpSwJCEaueJLmeTtpe2IRHy7b7XRFxphy4PHHH2fcuHG/PR81ahRjxoyhbdu2AHz88cfceOONDBw4kGbNmjFy5Mjf1p03bx7du3enc+fODBkyhLS0NN566y0SExPp06cPffr08UqNNnBlSWs3BJa/xTPHv6Xvwm4M7lSXOlVDna7KGOMtPzwBhzZ69zVrtYOrXil08dChQ3n44Ye57777APj666+ZMGECH3300W/rrFu3jrVr1xIcHEyLFi148MEHCQ0N5cUXX2T+/PmEh4fz6quvMnbsWJ555hnGjh3LokWLqF69ulc+goVLSfPzh77PUOPLW7iRhbw0uw7v3tbZ6aqMMWVYp06dOHz4MImJiSQnJxMZGUmDBg1+t07fvn2pUqUKAK1bt2bv3r2cOHGCzZs306NHDwCysrLo3r17idRo4VIaml8JDboz8uB3xGzowc9dG3DpRd7568AY47Aiehgl6eabb2bq1KkcOnSIoUOH/mF5cHDwbz/7+/uTk5ODqtK/f3++/PLLEq/PjrmUBhHoN4rw7KM8HLGQZ6dvIjs3z+mqjDFl2NChQ5kyZQpTp071+Cyxbt26sXz5cnbs2AFAeno627ZtA7w/RL+FS2lp0A2aX8Xf+J7Dhw/xyS97na7IGFOGtWnThtTUVOrWrUvt2rU9ahMdHc3HH3/MrbfeSvv27enWrRtbtmwBYNiwYVx11VVeO6DvyJD7IjIEGAW0Arqqamy+Ze2B94DKQB5wsapmnNV+FHA3kOye9W9VnZ1veQNgMzBKVUcXV4/XhtwvTtJmdPyl/BBxM4+nDGHBiN7UiAgp+fc1xniVDbnv4otD7scBNwJL888UkQDgM2C4qrYBLgcKu63jG6ra0T3NPnsZ8IN3S/aCmq2RDkMZmD6dqjmHefWHrU5XZIwxJcKRcFHVeFUt6DfrAGCDqq53r3dUVc/pykMRuQHYBfjmmCt9/o0fyjt15vLNmgRW7z3udEXGGON1vnbMpTmgIjJXRNaIyMgi1n1ARDaIyCQRiQQQkXDgceC54t5IRIaJSKyIxCYnJxe3uvdUbQBdh9E+eSY9Kh3k2elx5ObZTcWMKWsq0s0Az+ezlli4iMh8EYkrYBpURLMA4DLgdvfjYBHpW8B644GmQEfgIDDGPf85XLvL0oqrT1UnqmqMqsZER0efwyfzgp7/REKq8EbUNOIOpPDVqv2l+/7GmAsSEhLC0aNHK0TAqCpHjx4lJOTcjg+X2HUuqtrvPJolAEtU9QiAiMwGOgMLznrt30aBFJH3gZnup5cAN4vIa0BVIE9EMlT1nfOopeSERUHvkdSY+2+G1RnIa3MDuaptLSLDg5yuzBjjgXr16pGQkECp7vVwUEhICPXq1TunNr52EeVcYKSIhAFZQG9cB+d/R0Rqq+pB99PBuE4QQFV75ltnFJDmc8FyxsV3wcqJPMpnfJTxNGN+3MqLN7RzuipjjAcCAwNp3Lix02X4NEeOuYjIYBFJALoDs0RkLoCqHgfGAquAdcAaVZ3lbvOBiJw55e01EdkoIhuAPsAjpf0ZLlhAMPQbRcixLYy+aBNf/LqPuAMnna7KGGO8wpHrXHxNqV3ncjZV+LA/eSf20St9NLWiq/O/4d0RkdKvxRhjzpEvXudiwDUszICX8EtLYkLTX4jde5zv1h1wuipjjLlgFi5Oa3AJtB5Emz0fc3mdPF6evYXUjMKuGzXGmLLBwsUX9BuF5GYzpvpMjqRl8taC7U5XZIwxF8TCxRdENYGuw6i2/Wv+0SaLj5bvYcdh741Oaowxpc3CxVf0GgHBEdyf8wlhQf6Mmr65QlygZYwpnyxcfEVYFPQaSdCehYztlMyyHUeYu+mQ01UZY8x5sXDxJV2HQVRT+u57kzY1Q3lhZjyns85p3E5jjPEJFi6+JCAIrnwZObqdcc1Wc+DEacYv3uF0VcYYc84sXHxN8yuhaV8abnyb29qEMWHpLvYdTXe6KmOMOScWLr5GBK58GbLSeCp8GgF+wvMzNztdlTHGnBMLF19UoyV0vZuwjZ/y3CXK/PgkFm097HRVxhjjMQsXX3X5ExBSlZsOv0OTamE8P2MzmTl2cN8YUzZYuPiq0Ei44kn89i7j7c4H2H3kFB8u2+10VcYY4xELF1/W+U6o0Zo2G1/lqpaRvLNwBwdPnna6KmOMKZaFiy/zD4CB/4ET+/hP7SXk5Ckvz97idFXGGFMsCxdf1+RyaHktVWPfZkS3ysxYn8gvO486XZUxxhTJwqUsGPAi5GXz99OTqFs1lFHTN5GTm+d0VcYYUygLl7IgqjH0eAj/TVMZe0kaW5NS+XTFXqerMsaYQlm4lBWXPQpVGtA1/j9cflFVxv64jSNpmU5XZYwxBbJwKSuCwuCqV5DDm3m94a+czsrltTl2cN8Y45ssXMqSFldDswFErxrLQ10r8XVsAmv3HXe6KmOM+QMLl7JEBK56FXKzGJ41mRoRwTw7fRN5eXZTMWOMb7FwKWuimsBlDxO4eSpju6ayIeEkX8fud7oqY4z5HQuXsuiyR6BqA3pse4VuDSN4be5WTqZnO12VMcb8xsKlLAoMhateQ5K38GbjlZxIz2Lsj1udrsoYY35j4VJWtbgKmg+k1po3uLdLGJ+u2MvmxBSnqzLGGMDCpWwb+ArkZvNQzmSqhAYyavomVO3gvjHGeRYuZVlUY+j5KEFbpjG2yzFW7jnG9PWJTldljDEWLmXeZY9AtYu4fMd/iKkbwsuz40nLzHG6KmNMBWfhUtYFBMO1byLH9/BO3fkkpWTy9sLtTldljKngLFzKg8Y9oePt1IqbyANtspi0bDc7k9OcrsoYU4FZuJQXA16EkCo8dPpdQgPEDu4bYxzlSLiIyBAR2SQieSISc9ay9iLyi3v5RhEJKaD9KBE5ICLr3NPV59K+XAqLggEvEZi4ivGt4vhp+xHmbU5yuipjTAXlVM8lDrgRWJp/pogEAJ8Bw1W1DXA5UNil52+oakf3NPs82pc/HYZC415cuvstukXn8MLMzWRk5zpdlTGmAnIkXFQ1XlULuqR8ALBBVde71zuqqufy2/FC25dtInDNG0hOJu9EfU3C8dNMWLLT6aqMMRWQrx1zaQ6oiMwVkTUiMrKIdR8QkQ0iMklEIs+1vYgME5FYEYlNTk725mdwVvWLoNcIqu+dycim+xi/eCf7j6U7XZUxpoIpsXARkfkiElfANKiIZgHAZcDt7sfBItK3gPXGA02BjsBBYMw5tkdVJ6pqjKrGREdHn89H9F09HoLqLRiW+i7hksmLszY7XZExpoIpsXBR1X6q2raA6fsimiUAS1T1iKqmA7OBzgW8dpKq5qpqHvA+0PVc2pd7AcFw3ZsEpOxnUsN5zN2UxJJt5ah3Zozxeb62W2wu0F5EwtwH53sDf/izW0Rq53s6GNcJAh63rxAaXgoxf6dDwhdcXXU/z03fRFZOntNVGWMqCI/DRUTCRcTfG28qIoNFJAHoDswSkbkAqnocGAusAtYBa1R1lrvNB/lOW37NfZrxBqAP8Ehx7SukfqOQynV5PWgiCUdO8NHy3U5XZIypIKSwC+1ExA8Yiuv4xcVAJhAMJOPa3TRRVcvFOCMxMTEaGxvrdBklY/t8+PwmZla5jcePD2LhiMupWbliXPpjjClZIrJaVWMKWlZUz2URroPm/wJqqWp9Va0B9ARWAK+IyB1er9Z4V7N+0OE2rkn5iovydvGf2fFOV2SMqQACiljWT1X/cAGiqh4DvgG+EZHAEqvMeM+VLyE75vM+H3Hpurrc2rUBlzSp5nRVxphyrKieyzQRuU1EwgtboaDwMT4oLAquHUuNU9t4rNJcnp2+iZxcO7hvjCk5RYXL+8B1wG4R+UpEbhCRoFKqy3hbq+ug9Q3clfc12Ulb+GLlPqcrMsaUY4WGi6p+r6q3Ag2Bb4G/APvcV8T3L60CjRdd/Tp+wZWYEDGJsXPjOZqW6XRFxphyqthTkVX1tKp+paqDcY3d1QmYU+KVGe+rVAO56jWaZcUzJGcWo+cVNLybMcZcuGLDRURqisiDIrIc+A6YB3Qp6cJMCWk3BJoPZGTgV6yMXcH6/SecrsgYUw4VGi4icreILATW4BoQcqSqNlHVx1V1XWkVaLxMBK77LwHB4fw3aCKjvt9AXp7dVMwY411F9VwuBV4B6qvqg6q6vJRqMiUtohZy7Vjasp0eBz9h6poEpysyxpQzRR3Q/6uqzsM1hP0dIvIMgIg0EJGuhbUzZUTbG9E2N/Fw4Ld8N3s2J0/bWeXGGO/xZGyxcbjGALvV/TwVeLfEKjKlRq4ZjYZG8UzO27w1N674BsYY4yFPwuUSVb0fyIDfBoe0613Kg7AoAgePo6XffqJXj2XLoRSnKzLGlBOehEu2ezRkBRCRaMAu7y4vmg8gs/0d3O0/g8/+9z87uG+M8QpPwuUtYBpQQ0ReApYBL5doVaZUBV/zCqdD63BX8qu8PWe90+UYY8oBTy6i/BwYCfwH1y2Fb1DV/5V0YaYUBUcQ/qf3aOB3mKifX2D6+kSnKzLGlHFFXedS6czPqrpFVd9V1XdUNb6gdUzZJo17opfcx58D5jNr6iQ2Jpx0uiRjTBlWVM/lexEZIyK98o+MLCJNROTv7rtHDiz5Ek1p8e//LDnRrXnF/z0en/wjh1MznC7JGFNGFXWdS19gAXAPsElEUkTkKPAZUAv4i6pOLZ0yTakICCZgyEdU8c/kiay3Gf7JKjJzcp2uyhhTBhV5zEVVZ6vq7araSFUrq2o1Vb1UVV9S1UOlVaQpRTVa4nflS/SSdbRP/Jonp8VR2K2wjTGmMJ6cLWYqmovvguYDeSpoChvX/MyHy3Y7XZExpoyxcDF/JALXv4N/WFUmRbzHmNnrWbz1sNNVGWPKEAsXU7BK0cgN46mbtZtXKn/Dg1+uZWdymtNVGWPKCE/u5zJaRNqURjHGxzTrB5fcy6DMGfTxW8fdk2NtgEtjjEc86blsASaKyK8iMlxEqpR0UcaH9BsFNdowJug9Mo4l8OCXa8nJtdF/jDFF8+QK/Q9UtQfwf0AjYIOIfCEifUq6OOMDAkPg5kkE5p5mWq2PWbYtiVd+2OJ0VcYYH+fRMRf3wJUt3dMRYD3wqIhMKcHajK+o0RKuHk3NY6uY1GghHyzbzf9i9ztdlTHGhwUUt4KIjAWux3VB5cuqutK96FUR2VqSxRkf0ul22PMTvdd/xLB6zXlymh9NoivRpWGk05UZY3yQJz2XOKC9qt6TL1jOsDtSViRXj0aqN+OJ9DG0qXyaez5dzcGTp52uyhjjgzwJl3VASxHpnG9qKiIBqmqjG1YkwZVgyGT8stL4POpDsrKzufuTWE5n2RAxxpjf8/Q2xyuAicD7wC/AFGCbiAwowdqML6rZGq5+jbADy5jW7hc2Jabw2NT1NkSMMeZ3PAmXPUAnVY1R1S5AJ1y7yvoBr5VgbcZXdfoztPsTTTe9zRuXnGLmhoOMW7zT6aqMMT7Ek3BpqaqbzjxR1c24wmZXyZVlfJoIXDsWopowaMfT3NE2mNfnbmXeJhvL1Bjj4km4bBOR8SLS2z2Nc88LBs7rcm0RGSIim0QkT0RizlrWXkR+cS/fKCIhBbQfJSIHRGSde7raPT9QRCa728WLyL/Opz7jgeAIGDIZyTjJ81lj6FQ3nEe+WseWQylOV2aM8QGehMtfgB3Aw8AjwC7gTlzBcr4XUsYBNwJL888UkQBc94sZrqptgMspPMDeUNWO7mm2e94QIFhV2wFdgHtEpNF51miKU6stXPdf/Pb9zGcNZxMeHMDdn8Ry7FSW05UZYxxWZLi4L56coapjVHWwqt6gqqNVNV1V81T1vEYyVNV4VS3oGpkBwAZVXe9e76iqnsupSAqEu0MqFMgC7E/pktThFug6jPA17/FVj0SSUjK57/PVZNsQMcZUaMXdLCwXSC/F8cSaAyoic0VkjYiMLGLdB0Rkg4hMEpEzV/JNBU4BB4F9wGhVPVZQYxEZJiKxIhKbnJzs1Q9R4Qx4CepfQuPlT/Buv1BW7DrG8zM2O12VMcZBnuwWywA2isiHIvLWmam4RiIyX0TiCpgGFdEsALgMuN39OFhE+haw3nigKdARV5CMcc/vCuQCdYDGwD9FpElBb6SqE91nwMVER0cX93FMUQKCYMhkCAqn/8Z/8mCPmny6Yi+frdjrdGXGGIcUO/wLMMs9nRNV7Xfu5ZAALFHVIwAiMhvojGvomfyvnXTmZxF5H5jpfnobMEdVs4HDIrIciMF1nMiUpMq1YchHMPl6Ho1+g7jm/2DU9E00ja5E96bVnK7OGFPKPBkVeTLwNbBCVSefmUqonrlAexEJcx836Q38Yf+KiNTO93QwrhMEwLUr7ApxCQe64bplgCkNjS6DAS8gW2YyvskyGlYL477PV7P/WLrTlRljSpknNwu7DtcQMHPczzuKyPQLeVMRGSwiCUB3YJaIzAVQ1ePAWGCV+z3XqOosd5sP8p22/Jr7dOMNuM5Ye8Q9/12gEq6wWQV8pKobLqRWc4663QdtBhOy5EU+v/wUuXnKXZNjScvMcboyY0wpkuKG7RCR1cAVwGJV7eSet9F9um+5EBMTo7GxsU6XUX5kpsGkK+HEfmIHTOVPU5Pp16omE+7ogp+fOF2dMcZLRGS1qsYUtMyTA/o5BQxQaQNJmcIFV4KhX4B/ADE/38vzA+oxb3MSb8zf5nRlxphS4tGQ+yJyG+AvIs1E5G3g5xKuy5R1kQ3hls/g+B5uT3iOoV3q8PbCHcxYn+h0ZcaYUuBJuDwItAEygS9xXZT4cAnWZMqLhpfCNWOQnQt4MeJ/xDSM5LGp64k7YHdqMKa88+RssXRVfVJVL3ZfF/KkqmaURnGmHOhyJ3QdRsCKd5nUaTtRYUHc/UksyamZTldmjClBnpwt1lxEJorIPBFZeGYqjeJMOXHly9C4F5V/HMGnVwon0rMZ/tlqMnPsJmPGlFee7Bb7H7AWeAp4LN9kjGf8A11X8FeuS9MF9/DONdGs3nucp6bF2U3GjCmnPD1bbLyqrlTV1WemEq/MlC9hUXDrFMjJoO+aBxjRuzb/W53ApOV7nK7MGFMCPAmXGSJyn4jUFpGoM1OJV2bKnxot4U+fwJFt3J/8Ale1rsZLszazdJsNHGpMeePp/Vwew3X68Wr3ZFccmvPTtA9c+yayayFvVf6c5jUq8cAXa9iVfF53bzDG+ChPzhZrXMBU4EjDxnik85+h5z8JXPcJU9r8SoC/H3d9EsvJ0+d1Y1NjjA8qNFzy30tFRIactezlkizKVAB9noK2N1H155f4qsdB9h1N5x9friU3zw7wG1MeFNVzGZrv57PvRT+wBGoxFYmfHwwaB/W70Wz5CMb1ymHJtmRenWODWBtTHhQVLlLIzwU9N+bcBYa4xiCrUpcB6x/ikU4wcekuvlmd4HRlxpgLVFS4aCE/F/TcmPMTXg1unwrixz8SH+fqhvCvbzeyZt9xpyszxlyAosKlg4ikiEgqrht4peR7Xm6G2zc+oFpTuGMqcvo4b+W+yEWVc7jn09UcPHna6cqMMeep0HBRVX9VrayqEaoa4P75zPPA0izSVAB1OsEtnxFwbDtTq75NbmY6wz5ZTUa2DRFjTFnkyXUuxpSOpn3gxvcIO7iSOfUnE594jJFTN9gQMcaUQRYuxre0vQmuepUaB+bzfaNvmb7+AOOX7HS6KmPMOQpwugBj/uCSeyAtiTY/jeG9OuEMnys0rxFBv9Y1na7MGOMh67kY33TF09D5/7jy2Gc8GzmPh6asZVtSqtNVGWM8ZD0X45tE4No3ISudO+M+JjnAn7smB/P9/T2IDA9yujpjTDGs52J8l58/DJ4ALa7hsbwP6ZHyA/d9vobs3DynKzPGFMPCxfg2/0AY8hE07cvLAROpvmcGL87c7HRVxphiWLgY3xcQDLd8hjS8lP8GjSfx12/44td9TldljCmChYspG4LC4LavkLqdGBf0NvOnf8avu446XZUxphAWLqbsCI5A7piKX42WTAgcy6effcj+Y+lOV2WMKYCFiylbQiPxv3M6Wr0FY/NeZeKHEziVmeN0VcaYs1i4mLInLIrgv80gM7IFT6W9xEcfjSfPbjJmjE+xcDFlU1gUEcNmkVKlOcMOPsv0rz9wuiJjTD4WLqbsCo2k+r2zORTWjKvjHyd2zqdOV2SMcbNwMWWahEZS8/457Am6iA6/PMS+nz53uiRjDBYuphwIrhRJ1D2z2OzXjLoLHiD150lOl2RMhedIuIjIEBHZJCJ5IhJz1rL2IvKLe/lGEQkp5DUeFJGt7vVeyzf/XyKyw73sypL+LMY3VK8eTcBfvuNnbUfEvEfIWfaW0yUZU6E51XOJA24EluafKSIBwGfAcFVtA1wOZJ/dWET6AIOA9u71RrvntwaGAm2AgcA4EfEvuY9hfEmbRrVJGfwJs3K7EjD/aTLmPg92ozFjHOFIuKhqvKpuLWDRAGCDqq53r3dUVQu6z+29wCuqmule77B7/iBgiqpmqupuYAfQ1fufwPiqazo24lD/cXyV24eQX8aQ8OWDkGcDXRpT2nztmEtzQEVkroisEZGRRazXU0R+FZElInKxe35dYH++9RLc8/5ARIaJSKyIxCYnJ3vtAxjn/b1XM1oP+5ipQYOot+1T1rw1lNRTdiW/MaWpxMJFROaLSFwB06AimgUAlwG3ux8Hi0jfQtaLBLoBjwFfi4gAUsC6Be4XUdWJqhqjqjHR0dHn8tFMGdCuflWue2wSS+vdQ+cTc9k05mpWxO9xuixjKowSCxdV7aeqbQuYvi+iWQKwRFWPqGo6MBvoXMh636rLSiAPqO6eXz/fevWARO98IlPWBAcG0Ouu19h72atcnLeeSl8O4rWpi0nPsuFijClpvrZbbC7QXkTC3Af3ewMF3bzjO+AKABFpDgQBR4DpwFARCRaRxkAzYGVpFG58V8N+w8m55UuaByRx28a/M/yNL1i155jTZRlTrjl1KvJgEUkAugOzRGQugKoeB8YCq4B1wBpVneVu80G+05YnAU1EJA6YAvzF3YvZBHyNK5DmAPcXckKAqWCCWw0k6K4fqBEK755+gtcnfsSLMzeTkW1fD2NKgqidqklMTIzGxsY6XYYpDcf3kPfpTeQd38vDmcOJr9aP0UM60KlBpNOVGVPmiMhqVY0paJmv7RYzpmRFNsLvrh8JqB/DO0FvMzT9S24av5zX5mwhM8d6McZ4i4WLqXjCouD/vocOt3J37hSmRX/ApMWbGfTOcuIOnHS6OmPKBQsXUzEFBMMN46H/83RIWczK2qMJSDvIDe8u583528jOtQsvjbkQFi6m4hKBHg/BrVOofGov04Of5r5mJ3hz/nYGj1vO1kOpTldoTJll4WJMi4Hw9x/xCwzh0YSHmX7ZXg6eyOC6t5cxfvFOcu0ul8acMwsXYwBqtoa7F0H9rrSP/RfL207nypZVeXXOFm6e8DM7k9OcrtCYMsXCxZgzwqvBn7+DHg8Rsv4T3jr9b94fVJPdR05x9X9/4sNlu8mzXowxHrFwMSY//wDo/zzc8hlyZDv9lw5h0WDlsouq88LMzQyduIK9R085XaUxFy4vl9OLxnLs1y9L5OUtXIwpSKvrYNhiiKhF5De38EHjRbx+UxviD6Zw1X9/4tMVe7ELkE1ZdXT/Fva/0YfQJc8Rv6yo4R7Pn4WLMYWpfhHcNR/a3Ywseokhmx7gx7ub06VhJE9/F8efP1zJgROnna7SGI8lHEtj9vvPEPpBL6qkbGdyrX8TOfS9EnkvG/4FG/7FFEMV1n4GP4yEgBD0hnF8caI1L82Kx1+Ep69tzZCYerju+mCM79mZnMbUuYvou+0FYvy2El+pG2E3vU3Dxs0v6HWLGv7FwgULF+Oh5G0w9W+QtBEuuZf9XR5nxLQt/Lr7GH1aRPPKTe2pWTnE6SqN+c2mxJNMWLSNOvEf8oj/VDQghNP9Xiaq259d13ldIAuXYli4GI9lZ8D8UfDreKjVjrzB7zN5RwivztlCcIA/z13fhkEd61gvxjhq9d5jvLtoJ/u3rmFs0Hu0k51kXnQVwYPehIhaXnsfC5diWLiYc7Z1Dnx/H2SmQd+n2dX0/xjxTRxr9p3gyjY1eWlwO6pXCna6SlOBqCrLdhzh3UU7WLfrICNDvucvMgMJqYrfNa9Dmxu90lvJz8KlGBYu5rykHYaZj8CWmVC/G7mDxvHBJhgzbxuVQgJ48Ya2XN2uttNVmnIuL0/5MT6JcYt2sD7hJDdWiuO5gI+JyEiEjne4Tq0Pr1Yi723hUgwLF3PeVGHDVzB7JORlQ//n2d7gT/xz6kY2JJzkug51eP76NkSGBzldqSlncnLzmLnhIOMW72BbUhpdqqbzRpUpNEiaD9Et4Zqx0KhHidZg4VIMCxdzwU4egOkPws4F0LgX2VeNZcJGeGvhdqqGBfGfwe3o17qm01WaciAzJ5dvVh9gwpKd7DuWTusawbxWfwVtto9H8nKh90jo/gAElPwfNBYuxbBwMV6hCmsmw7xnICcDej/G5sZ38ug38Ww5lMpNnevxzHWtqRIa6HSlpgxKz8rhi1/38f5Pu0hKyaR93cqMaplApy2jkaM7oNkAuPp1iGxUajVZuBTDwsV4Veoh+OFx2PwdRLci++o3+O+2KMYv2UmNiGBeuak9vZtHO12lKSNOns7m01/2MGn5Ho6dyqJbkyge66x0jn8d2bkQqjWDK1+G5gNKvTYLl2JYuJgSsXUOzB4BJ/dDzN+Ia/kwD0/fw47DadzatQFPXtOKSsEBTldpfNSRtEwmLdvNp7/sJTUzhyta1uAfl1aj444JsOoDCK4EvZ+ArneDvzO9YQuXYli4mBKTmQaL/wMrxkFIVbJ7/5sxR7vz3rK91K0ayms3t+fSptWdrtL4kMQTp5m4dBdTVu0jMyePq9vW5v6edWi9bwosewMyU6DLX6HPkyV2FpinLFyKYeFiStyhOJjzBOz5CWq2ZWvHJ7lnWSh7jqZz56WNeHxgS0KD/J2u0jho95FTTFi8k2/XJqAKN3Sqy709G9A04TtY8iqkHnQdV+k3Cmq2cbpcwMKlWBYuplSowubvYd7TcHIfOS2v5x3/v/Dm6kwaVQtjzJ860KVhlNNVmlIWfzCFcYt3MmtDIgH+fgy9uD7DejaiXuI8WPgiHNsJ9S+Bvs+W+KnF58rCpRgWLqZUZZ+Gn9+Gn8aC5pLY7HaG7enNppNB3N2zCY/2b05IoPViyrs1+44zbtEO5scfJjzInzu6N+TvPRpRI3Ghq6dycD1Et4J+z0LzgV6/ut4bLFyKYeFiHHHygOt4zLrP0cBQFkTewkN7e1C7RjRjhnSgQ/2qTldovExV+WXnUd5ZtIOfdx6lalggf720MXd2b0CVPT/A0tGugVEjG0Hvx6H9LeDnu39oWLgUw8LFOCp5q2v3R/x0soIjeTfnBt4/fTl/692Kf/RtRlCA3XaprFNVFsQf5p1FO1i3/wQ1IoK5u2cTbru4LuE7ZsDS1yF5C1S7CHo9Bm1vdt0V1cdZuBTDwsX4hITVsGAU7F5KSkAUb52+itjqgxjWz3VdTLidtlzm5OYpszYeZNyiHWw5lEq9yFCG927Kze0iCdn0lesswmO7XMO19HoM2gz26Z7K2SxcimHhYnzK7qWu3SO7l3CCCN7PvoopciXtmzagf+ta9GtdgxoRdt8YX5aVk8e0tQmMX7yTPUfTuahGJe67vCnXN/EjYPUHEDsJTh+Hul2gx0PQ8jrwK3s9VAuXYli4GJ+0fyV5S1/Hb/s8MvzCmep3JW+nXUESUXRqUJX+rWsyoHVNmkZXsvvH+IjTWblMWbWPiUt3cfBkBu3qVuH+Pk0ZEJWM38oJsPF/kJsNLa9xjf/VoJtPHqj3lIVLMSxcjE87uB6WjkbjZ4CfP1ur9WNi1pV8m+QaCLNx9fDfgqZTg0j8/cruL6uyKiUjm09/2cukZbs5eiqLro2jeLBnPS7LXIqs/ggSVkFgGHS8HbrdC9WaOl2yV1i4FMPCxZQJx3bDyvdhzSeQlUpW7YtZEX0zk461ZfnuFLJzlWrhQfRtVYP+rWvRs1l1O6W5hB07lcWkZbuZ/MseUjNyuLxFNP/sLLQ7+C2s+wIyTkD15hDzN+gwFEIjnS7ZqyxcimHhYsqUjBRY9zn8OgGO74GwamS2vYXlla9h2v5wFm85TGpmDqGB/vRsVp3+rWvSt1VNouyeMl5z6GQGE5fu4suV+8jIyeXGVuE8WmcTdfd+D/t/Bb9AaHUdXPx3aNijTO/6KorPhYuIDAFGAa2Arqoam29Ze+A9oDKQB1ysqhkFvMaDwANADjBLVUeKSH/gFSAIyAIeU9WFxdVj4WLKpLxc2LkI1nwMW3+AvBxo0J2cDnewMrQHP2w7xfz4JA6ezMBPIKZhFAPa1KR/65o0rBbudPVlhqqy/9hpNh44SVziSeIOnGTFrqP4aw4jm+7jT4HLqLR3AeRmuS567HCLa/dXpRpOl17ifDFcWuEKjveAEWfCRUQCgDXAn1V1vYhUA06oau5Z7fsATwLXqGqmiNRQ1cMi0glIUtVEEWkLzFXVusXVY+Fiyry0w67dMGs+cQ0XEhACzQeibW9iU9glzNt2gnmbk9hyKBWA5jUr0b91Tfq3rkX7ulXws+M0gOuWwXuPpbuCJN+UkpEDQLh/DkMidzIoeDXtTy3HP+M4hEdDuyGu3V612pfbXkpBfC5cfntzkcX8PlyuBm5T1TuKafc1MFFV5xexjgBHgDqqmlnU61m4mHJD1bVbZuNU2DQN0o9AcBXXLppW15EQeTFzt6Xw4+ZDrNpznNw8pWblYPq1cvVoujetRnBAxThOk5un7D6SRtyBlN/CZHNiCqmZriAJ8vejZe0IOtUKol/gRtqlLKVKwgIkMxWCK7uGZGk3BJpeUSYueCwJZSlcHga6ADWAaGCKqr5WQLt1wPfAQCDD/RqrzlrnZmC4qvYr5L2HAcMAGjRo0GXv3r3e+VDG+IrcHNi9GDZ+A1tmuoZqDwh1/TJscRUn6vVhYQL8uDmJJduSSc/KpVJwAL2bR9O/dU36tKhBlbDycdfMnNw8diaf+l2PZPPBFNKzXDtFggP8aF2nMm3rVKFdncp0DjtE4xMr8N+1APb+7NrlFRrlOoW41fXQpDcEBDv8qZznSLiIyHygVgGLnlTV793rLOb34TICuB+4GEgHFgBPqeqCs147DlgIPORe9yugibo/jIi0AaYDA1R1Z3G1Ws/FlHs5WbB3mevYzJbZkJIACNRuD417k9WgJz/nNGPutlR+3HyYI2mZBPgJlzSJon+rmvRvU4u6VUOd/hQeyc7NY1tSKpvO9EgSTxJ/MIWM7DwAwoL8aV27Mm3rVqFt3Sq0q1uFpkHHCUhY4bolwo6F7u2D6xjKRX1dQ9037FFheyiFKUs9l6HAQFW90/38aSBDVV8/q90c4BVVXex+vhPopqrJIlIPV/D8VVWXe1KHhYupUFTh0EbYNsd1QkDCKsjLdp3hVL8r2uBSdgS3ZtaxuszYfpqdyacAaFOnsvs4TU1a167sExduZubksu1Q2u8Otm85mEpWritIKgUH0KZO5d9CpG3dyjSOCsX/2A5IWOnqlexdDif2uV4wuAo06QUX9YOmfaFqfQc/ne8rS+ESiau3chmus73mAG+o6qyz2g3HdSzlGRFp7m7TAKgCLAGeV9VvPK3DwsVUaFmnYO8vrl1ou5ZAUhyo65czUU1Jje7E+rwm/HikGtMPVeW4RlC3auhvF25e3DiKQP+SH7okIzuX+IMpxCWmEJfgCpNtSalk57p+h1UOCfgtRNq4HxtWCcDv+C7XhaiJayFxnStYs12BSVh1aHipq1fS8FLXTbjK0NheTvO5cBGRwcDbuI6rnADWqeqV7mV3AP8CFJitqiPd8z8AJqhqrIgEAZOAjrhCaISqLhSRp9xtt+d7uwGqerioeixcjMknM831izhhFSTEuv7CP5X82+LTwdHs9GvAylO12JVbg6OBtanXpBWd23WgZ+u6VPLCAJvpWTnEH0xhY8JJV5gcOMn2w2nk5rl+X0WGBf62W6t97XDaVzlNHTmCHN8DR7ZB8jY4stV14emZk00Dw6BWO6jTCWp3dI3rVb1ZhTq7y9t8Llx8jYWLMUVQhdRDcHgTJG2Gw5shKQ49sh3J+f+XoOWpkEQkp4OjCapai2o16hEaVQfCa0BIZQiqBMGVICjC9egfCOLHqWzYlpTGlsOn2HbwJLsPHiXp+AkCNZsQsqgdmkubyByaRWRTPySDWoHphOelICmJcDLBdfvfMz0tcO3eq9bUFRzVW7iukK/d3vVovRKvsnAphoWLMechLw/SkuD4HnKP7ubgnniOHdhOxomDVMo+TrScIEpS8Sev+NfylPhBSFUIi4LKdaByPaiSb6ra0HWjLTvwXiqKChf7FzDGnB8/P6hcGyrXxr9hd+p1hnq4rmjflpTGV5sPsWBTIvsPHKCSpFM7JIe8jDTCJINKnCY6zI/6kcHUrxpCvaoh1KkSROWwENcFoAHB7scQCAp3jckVGukKljI4NH1FZOFijPEqEaFFrQha1IrggSuacehkBvPjk1i3/wSNq4e7jpXUqUy1SnadSHlm4WKMKVG1qoRwR7eG3NGtodOlmFJk/UtjjDFeZ+FijDHG6yxcjDHGeJ2FizHGGK+zcDHGGON1Fi7GGGO8zsLFGGOM11m4GGOM8TobWwwQkWSgvNyKsjqu2zubwtk2Kp5tI89U9O3UUFWjC1pg4VLOiEhsYQPJGRfbRsWzbeQZ206Fs91ixhhjvM7CxRhjjNdZuJQ/E50uoAywbVQ820aese1UCDvmYowxxuus52KMMcbrLFyMMcZ4nYVLGSEi9UVkkYjEi8gmEXnIPT9KRH4Uke3ux8hC2g8Uka0iskNEnijd6kuHF7bRHhHZKCLrRCS2dKsvHUVsoyHu53kiUuiptRX8e+TpNir33yNP2DGXMkJEagO1VXWNiEQAq4EbgDuBY6r6ivs/e6SqPn5WW39gG9AfSABWAbeq6uZS/Agl7kK2kbv9HiBGVcvtRXFFbCMF8oD3gBGq+odfivY9Kn4budvvoZx/jzxhPZcyQlUPquoa98+pQDxQFxgETHavNhnXf4KzdQV2qOouVc0CprjblSsXuI0qhMK2karGq+rWYppX6O+Rh9vIuFm4lEEi0gjoBPwK1FTVg+D6TwHUKKBJXWB/vucJ7nnl1nlsI3D9ZTpPRFaLyLBSKdRBZ20jT1T075GnKtT3qDABThdgzo2IVAK+AR5W1RQR8ahZAfPK7f7Q89xGAD1UNVFEagA/isgWVV1aYoU66Oxt5GmzAuZVmO/ROTStMN+joljPpQwRkUBcX/bPVfVb9+wk9z7iM/uKDxfQNAGon+95PSCxJGt1ygVsI1Q10f14GJiGazdQuVPINvJERf8eeaSifI+KY+FSRojrz+8PgXhVHZtv0XTgL+6f/wJ8X0DzVUAzEWksIkHAUHe7cuVCtpGIhLsP3iIi4cAAIK5kKy59RWwjT1T075EnbSvE98gjqmpTGZiAy3DtgtgArHNPVwPVgAXAdvdjlHv9OsDsfO2vxnWmz07gSac/j69tI6AJsN49baqA22gwrp5JJpAEzLXv0blvo4ryPfJkslORjTHGeJ3tFjPGGON1Fi7GGGO8zsLFGGOM11m4GGOM8ToLF2OMMV5n4WKMMcbrLFyMMcZ4nYWLMRWAiLwtImtE5GL381YiMkFEporIvU7XZ8ofCxdjyjn3MCQ1gHuAawHUNXz8cOBPQKE3vjLmfFm4GOMBEVksIleeNe9hERlXRJu0kq/sD+8ZKiJL3Df2AkBVTwG1gcXAW/nWvR5YhmtIHEQkSESWioiNlm4umIWLMZ75EtdAjfkNdc/3JX8DvlXV3DMzRKQaEAakAr/NV9XpqnopcLv7eRauoLmlVCs25ZKFizGemQpcKyLB8NtNpOoAy0TkURGJc08Pn91QRBqJSFy+5yNEZFS+ZVtE5AN3+89FpJ+ILBeR7SLS1b3eHSKy0n1f9vfy90zOcjt/HPX5KWA0roEUW7tf73IReUtE3gNm51v3O/drGHNBLFyM8YCqHgVWAgPds4YCXwGdgb8ClwDdgLtFpNM5vvxFwH+B9kBL4DZcI/OOAP4tIq1w9SZ6qGpHXL2PPwSAexj8Jqq6J9+8RsCl7lrjgTbuz7NYVf+hqveo6rv5XiYOuPgc6zfmDyxcjPFc/l1jZ3aJXQZMU9VTqpoGfAv0PMfX3a2qG1U1D1fvYoG6hivfCDQC+gJdgFUiss79vEkBr1MdOHHWvBeB592v91u4FMa9Oy3rzD1JjDlfduDOGM99B4wVkc5AqKquEZFeHrTL4fd/yIWctTwz3895+Z7n4fo/KsBkVf1XMe9zOv9ri0hH4EbgMhF5171sowf1BgMZHqxnTKGs52KMh9w9k8XAJP7/gfylwA0iEuY+5Xcw8NNZTZOAGiJSzX3M5tpzfOsFwM3ue7IjIlEi0rCA+o4D/iJyJmBeBa5T1Uaq2gjoQDE9F/fB/2RVzT7HGo35Heu5GHNuvsS162sogLv38jGu4zEAH6jq2vwNVDVbRJ4HfgV2A1vO5Q1VdbOIPAXMExE/IBu4H9hbwOrzcPVU8oBwVV2Q73WS3LfhjVLVY4W8XR9+f4DfmPNid6I0phxxn0zwqKr++Tzbfwv8S1W3ercyU9HYbjFjyhF3r2lREacqF8p9ttl3FizGG6znYowxxuus52KMMcbrLFyMMcZ4nYWLMcYYr7NwMcYY43UWLsYYY7zOwsUYY4zX/T+6ysPO7M/VsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vol_lst = np.linspace(np.min(volume_lst), np.max(volume_lst), 1000)\n",
    "plt.plot(volume_lst, energy_lst, label='dft')\n",
    "plt.plot(vol_lst, murn.fit_module.vinet_energy(vol_lst, e0, b0/ 160.21766208, bP, v0), label='vinet')\n",
    "plt.xlabel('Volume ($\\AA ^ 3$)')\n",
    "plt.ylabel('Energy (eV)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Murnaghan Module \n",
    "Besides the fitting capabilities the Murnaghan module can also be used to run a set of calculations. For this we define a reference job, which can be either a Vasp calculation or any other pyiron job type and then specify the input parameters for the Murnaghan job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.644025Z",
     "start_time": "2019-09-04T12:58:37.532255Z"
    }
   },
   "outputs": [],
   "source": [
    "job_vasp_strain = pr.create_job(job_type=pr.job_type.Gpaw, job_name='gpaw')\n",
    "job_vasp_strain.set_encut(320)\n",
    "job_vasp_strain.structure = basis.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.895833Z",
     "start_time": "2019-09-04T12:58:37.675200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Value</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>num_points</td>\n",
       "      <td>11</td>\n",
       "      <td>number of sample points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fit_type</td>\n",
       "      <td>polynomial</td>\n",
       "      <td>['polynomial', 'birch', 'birchmurnaghan', 'murnaghan', 'pouriertarantola', 'vinet']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fit_order</td>\n",
       "      <td>3</td>\n",
       "      <td>order of the fit polynom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vol_range</td>\n",
       "      <td>0.1</td>\n",
       "      <td>relative volume variation around volume defined by ref_ham</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Parameter       Value  \\\n",
       "0  num_points          11   \n",
       "1    fit_type  polynomial   \n",
       "2   fit_order           3   \n",
       "3   vol_range         0.1   \n",
       "\n",
       "                                                                               Comment  \n",
       "0                                                              number of sample points  \n",
       "1  ['polynomial', 'birch', 'birchmurnaghan', 'murnaghan', 'pouriertarantola', 'vinet']  \n",
       "2                                                             order of the fit polynom  \n",
       "3                           relative volume variation around volume defined by ref_ham  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "murn = pr.create_job(job_type=pr.job_type.Murnaghan, job_name='murn')\n",
    "murn.ref_job = job_vasp_strain\n",
    "murn.input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We modify the input parameters to agree with the settings used in the examples above and execute the simulation by calling the run command on the murnaghan job object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.933789Z",
     "start_time": "2019-09-04T12:58:37.931262Z"
    }
   },
   "outputs": [],
   "source": [
    "murn.input['num_points'] = 7\n",
    "murn.input['vol_range'] = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:37.983849Z",
     "start_time": "2019-09-04T12:58:37.935426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ase.atoms.Atoms"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(murn.structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:58:38.465443Z",
     "start_time": "2019-09-04T12:58:37.985315Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
       "      <th>subjob</th>\n",
       "      <th>projectpath</th>\n",
       "      <th>project</th>\n",
       "      <th>timestart</th>\n",
       "      <th>timestop</th>\n",
       "      <th>totalcputime</th>\n",
       "      <th>computer</th>\n",
       "      <th>hamilton</th>\n",
       "      <th>hamversion</th>\n",
       "      <th>parentid</th>\n",
       "      <th>masterid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_95</td>\n",
       "      <td>/gpaw_0_95</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:24:24.200176</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9666666666666667</td>\n",
       "      <td>/gpaw_0_9666666666666667</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:26:45.417210</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9833333333333333</td>\n",
       "      <td>/gpaw_0_9833333333333333</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:28:37.112334</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0</td>\n",
       "      <td>/gpaw_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:30:26.714705</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0166666666666666</td>\n",
       "      <td>/gpaw_1_0166666666666666</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:31:58.800251</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0333333333333334</td>\n",
       "      <td>/gpaw_1_0333333333333334</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:34:47.304029</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_05</td>\n",
       "      <td>/gpaw_1_05</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/</td>\n",
       "      <td>2020-10-02 17:36:23.322563</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_95</td>\n",
       "      <td>/gpaw_0_95</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_270/</td>\n",
       "      <td>2020-10-02 17:38:00.805999</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9666666666666667</td>\n",
       "      <td>/gpaw_0_9666666666666667</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_270/</td>\n",
       "      <td>2020-10-02 17:40:27.023982</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9833333333333333</td>\n",
       "      <td>/gpaw_0_9833333333333333</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_270/</td>\n",
       "      <td>2020-10-02 17:42:55.820191</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0</td>\n",
       "      <td>/gpaw_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_270/</td>\n",
       "      <td>2020-10-02 17:45:10.442772</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0166666666666666</td>\n",
       "      <td>/gpaw_1_0166666666666666</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_270/</td>\n",
       "      <td>2020-10-02 17:47:59.450726</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0333333333333334</td>\n",
       "      <td>/gpaw_1_0333333333333334</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_270/</td>\n",
       "      <td>2020-10-02 17:51:07.518608</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_05</td>\n",
       "      <td>/gpaw_1_05</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_270/</td>\n",
       "      <td>2020-10-02 17:54:45.224784</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_95</td>\n",
       "      <td>/gpaw_0_95</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_280/</td>\n",
       "      <td>2020-10-02 17:58:11.528057</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9666666666666667</td>\n",
       "      <td>/gpaw_0_9666666666666667</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_280/</td>\n",
       "      <td>2020-10-02 18:00:11.919363</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9833333333333333</td>\n",
       "      <td>/gpaw_0_9833333333333333</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_280/</td>\n",
       "      <td>2020-10-02 18:02:25.229474</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0</td>\n",
       "      <td>/gpaw_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_280/</td>\n",
       "      <td>2020-10-02 18:05:13.598633</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0166666666666666</td>\n",
       "      <td>/gpaw_1_0166666666666666</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_280/</td>\n",
       "      <td>2020-10-02 18:08:06.130672</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0333333333333334</td>\n",
       "      <td>/gpaw_1_0333333333333334</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_280/</td>\n",
       "      <td>2020-10-02 18:11:21.717226</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_05</td>\n",
       "      <td>/gpaw_1_05</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_280/</td>\n",
       "      <td>2020-10-02 18:14:19.003564</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_95</td>\n",
       "      <td>/gpaw_0_95</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_290/</td>\n",
       "      <td>2020-10-02 18:16:48.228097</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9666666666666667</td>\n",
       "      <td>/gpaw_0_9666666666666667</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_290/</td>\n",
       "      <td>2020-10-02 18:19:42.602848</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9833333333333333</td>\n",
       "      <td>/gpaw_0_9833333333333333</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_290/</td>\n",
       "      <td>2020-10-02 18:22:33.879253</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0</td>\n",
       "      <td>/gpaw_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_290/</td>\n",
       "      <td>2020-10-02 18:25:12.937586</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0166666666666666</td>\n",
       "      <td>/gpaw_1_0166666666666666</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_290/</td>\n",
       "      <td>2020-10-02 18:27:18.445423</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0333333333333334</td>\n",
       "      <td>/gpaw_1_0333333333333334</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_290/</td>\n",
       "      <td>2020-10-02 18:29:51.108935</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_05</td>\n",
       "      <td>/gpaw_1_05</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_290/</td>\n",
       "      <td>2020-10-02 18:33:09.633165</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_95</td>\n",
       "      <td>/gpaw_0_95</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_300/</td>\n",
       "      <td>2020-10-02 18:35:24.100573</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9666666666666667</td>\n",
       "      <td>/gpaw_0_9666666666666667</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_300/</td>\n",
       "      <td>2020-10-02 18:37:51.134146</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9833333333333333</td>\n",
       "      <td>/gpaw_0_9833333333333333</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_300/</td>\n",
       "      <td>2020-10-02 18:40:15.407176</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0</td>\n",
       "      <td>/gpaw_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_300/</td>\n",
       "      <td>2020-10-02 18:42:27.007123</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0166666666666666</td>\n",
       "      <td>/gpaw_1_0166666666666666</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_300/</td>\n",
       "      <td>2020-10-02 18:45:20.422390</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0333333333333334</td>\n",
       "      <td>/gpaw_1_0333333333333334</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_300/</td>\n",
       "      <td>2020-10-02 18:47:26.819490</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_05</td>\n",
       "      <td>/gpaw_1_05</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_300/</td>\n",
       "      <td>2020-10-02 18:49:24.101232</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_95</td>\n",
       "      <td>/gpaw_0_95</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_310/</td>\n",
       "      <td>2020-10-02 18:51:11.902579</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9666666666666667</td>\n",
       "      <td>/gpaw_0_9666666666666667</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_310/</td>\n",
       "      <td>2020-10-02 18:53:53.696423</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9833333333333333</td>\n",
       "      <td>/gpaw_0_9833333333333333</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_310/</td>\n",
       "      <td>2020-10-02 18:55:31.120613</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0</td>\n",
       "      <td>/gpaw_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_310/</td>\n",
       "      <td>2020-10-02 18:57:12.122217</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0166666666666666</td>\n",
       "      <td>/gpaw_1_0166666666666666</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_310/</td>\n",
       "      <td>2020-10-02 18:58:42.202686</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0333333333333334</td>\n",
       "      <td>/gpaw_1_0333333333333334</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_310/</td>\n",
       "      <td>2020-10-02 19:00:25.512077</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_05</td>\n",
       "      <td>/gpaw_1_05</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_310/</td>\n",
       "      <td>2020-10-02 19:02:08.222775</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_95</td>\n",
       "      <td>/gpaw_0_95</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_320/</td>\n",
       "      <td>2020-10-02 19:03:42.105391</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9666666666666667</td>\n",
       "      <td>/gpaw_0_9666666666666667</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_320/</td>\n",
       "      <td>2020-10-02 19:05:35.407201</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_0_9833333333333333</td>\n",
       "      <td>/gpaw_0_9833333333333333</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_320/</td>\n",
       "      <td>2020-10-02 19:07:21.099215</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0</td>\n",
       "      <td>/gpaw_1_0</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_320/</td>\n",
       "      <td>2020-10-02 19:08:51.322535</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0166666666666666</td>\n",
       "      <td>/gpaw_1_0166666666666666</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_320/</td>\n",
       "      <td>2020-10-02 19:10:14.501550</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_0333333333333334</td>\n",
       "      <td>/gpaw_1_0333333333333334</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_320/</td>\n",
       "      <td>2020-10-02 19:11:57.005028</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>finished</td>\n",
       "      <td>None</td>\n",
       "      <td>gpaw_1_05</td>\n",
       "      <td>/gpaw_1_05</td>\n",
       "      <td>None</td>\n",
       "      <td>/home/jovyan/thermo/encut_320/</td>\n",
       "      <td>2020-10-02 19:13:34.919295</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1</td>\n",
       "      <td>GpawJob</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id    status chemicalformula                      job  \\\n",
       "0    1  finished            None                gpaw_0_95   \n",
       "1    2  finished            None  gpaw_0_9666666666666667   \n",
       "2    3  finished            None  gpaw_0_9833333333333333   \n",
       "3    4  finished            None                 gpaw_1_0   \n",
       "4    5  finished            None  gpaw_1_0166666666666666   \n",
       "5    6  finished            None  gpaw_1_0333333333333334   \n",
       "6    7  finished            None                gpaw_1_05   \n",
       "7    8  finished            None                gpaw_0_95   \n",
       "8    9  finished            None  gpaw_0_9666666666666667   \n",
       "9   10  finished            None  gpaw_0_9833333333333333   \n",
       "10  11  finished            None                 gpaw_1_0   \n",
       "11  12  finished            None  gpaw_1_0166666666666666   \n",
       "12  13  finished            None  gpaw_1_0333333333333334   \n",
       "13  14  finished            None                gpaw_1_05   \n",
       "14  15  finished            None                gpaw_0_95   \n",
       "15  16  finished            None  gpaw_0_9666666666666667   \n",
       "16  17  finished            None  gpaw_0_9833333333333333   \n",
       "17  18  finished            None                 gpaw_1_0   \n",
       "18  19  finished            None  gpaw_1_0166666666666666   \n",
       "19  20  finished            None  gpaw_1_0333333333333334   \n",
       "20  21  finished            None                gpaw_1_05   \n",
       "21  22  finished            None                gpaw_0_95   \n",
       "22  23  finished            None  gpaw_0_9666666666666667   \n",
       "23  24  finished            None  gpaw_0_9833333333333333   \n",
       "24  25  finished            None                 gpaw_1_0   \n",
       "25  26  finished            None  gpaw_1_0166666666666666   \n",
       "26  27  finished            None  gpaw_1_0333333333333334   \n",
       "27  28  finished            None                gpaw_1_05   \n",
       "28  29  finished            None                gpaw_0_95   \n",
       "29  30  finished            None  gpaw_0_9666666666666667   \n",
       "30  31  finished            None  gpaw_0_9833333333333333   \n",
       "31  32  finished            None                 gpaw_1_0   \n",
       "32  33  finished            None  gpaw_1_0166666666666666   \n",
       "33  34  finished            None  gpaw_1_0333333333333334   \n",
       "34  35  finished            None                gpaw_1_05   \n",
       "35  36  finished            None                gpaw_0_95   \n",
       "36  37  finished            None  gpaw_0_9666666666666667   \n",
       "37  38  finished            None  gpaw_0_9833333333333333   \n",
       "38  39  finished            None                 gpaw_1_0   \n",
       "39  40  finished            None  gpaw_1_0166666666666666   \n",
       "40  41  finished            None  gpaw_1_0333333333333334   \n",
       "41  42  finished            None                gpaw_1_05   \n",
       "42  43  finished            None                gpaw_0_95   \n",
       "43  44  finished            None  gpaw_0_9666666666666667   \n",
       "44  45  finished            None  gpaw_0_9833333333333333   \n",
       "45  46  finished            None                 gpaw_1_0   \n",
       "46  47  finished            None  gpaw_1_0166666666666666   \n",
       "47  48  finished            None  gpaw_1_0333333333333334   \n",
       "48  49  finished            None                gpaw_1_05   \n",
       "\n",
       "                      subjob projectpath                         project  \\\n",
       "0                 /gpaw_0_95        None            /home/jovyan/thermo/   \n",
       "1   /gpaw_0_9666666666666667        None            /home/jovyan/thermo/   \n",
       "2   /gpaw_0_9833333333333333        None            /home/jovyan/thermo/   \n",
       "3                  /gpaw_1_0        None            /home/jovyan/thermo/   \n",
       "4   /gpaw_1_0166666666666666        None            /home/jovyan/thermo/   \n",
       "5   /gpaw_1_0333333333333334        None            /home/jovyan/thermo/   \n",
       "6                 /gpaw_1_05        None            /home/jovyan/thermo/   \n",
       "7                 /gpaw_0_95        None  /home/jovyan/thermo/encut_270/   \n",
       "8   /gpaw_0_9666666666666667        None  /home/jovyan/thermo/encut_270/   \n",
       "9   /gpaw_0_9833333333333333        None  /home/jovyan/thermo/encut_270/   \n",
       "10                 /gpaw_1_0        None  /home/jovyan/thermo/encut_270/   \n",
       "11  /gpaw_1_0166666666666666        None  /home/jovyan/thermo/encut_270/   \n",
       "12  /gpaw_1_0333333333333334        None  /home/jovyan/thermo/encut_270/   \n",
       "13                /gpaw_1_05        None  /home/jovyan/thermo/encut_270/   \n",
       "14                /gpaw_0_95        None  /home/jovyan/thermo/encut_280/   \n",
       "15  /gpaw_0_9666666666666667        None  /home/jovyan/thermo/encut_280/   \n",
       "16  /gpaw_0_9833333333333333        None  /home/jovyan/thermo/encut_280/   \n",
       "17                 /gpaw_1_0        None  /home/jovyan/thermo/encut_280/   \n",
       "18  /gpaw_1_0166666666666666        None  /home/jovyan/thermo/encut_280/   \n",
       "19  /gpaw_1_0333333333333334        None  /home/jovyan/thermo/encut_280/   \n",
       "20                /gpaw_1_05        None  /home/jovyan/thermo/encut_280/   \n",
       "21                /gpaw_0_95        None  /home/jovyan/thermo/encut_290/   \n",
       "22  /gpaw_0_9666666666666667        None  /home/jovyan/thermo/encut_290/   \n",
       "23  /gpaw_0_9833333333333333        None  /home/jovyan/thermo/encut_290/   \n",
       "24                 /gpaw_1_0        None  /home/jovyan/thermo/encut_290/   \n",
       "25  /gpaw_1_0166666666666666        None  /home/jovyan/thermo/encut_290/   \n",
       "26  /gpaw_1_0333333333333334        None  /home/jovyan/thermo/encut_290/   \n",
       "27                /gpaw_1_05        None  /home/jovyan/thermo/encut_290/   \n",
       "28                /gpaw_0_95        None  /home/jovyan/thermo/encut_300/   \n",
       "29  /gpaw_0_9666666666666667        None  /home/jovyan/thermo/encut_300/   \n",
       "30  /gpaw_0_9833333333333333        None  /home/jovyan/thermo/encut_300/   \n",
       "31                 /gpaw_1_0        None  /home/jovyan/thermo/encut_300/   \n",
       "32  /gpaw_1_0166666666666666        None  /home/jovyan/thermo/encut_300/   \n",
       "33  /gpaw_1_0333333333333334        None  /home/jovyan/thermo/encut_300/   \n",
       "34                /gpaw_1_05        None  /home/jovyan/thermo/encut_300/   \n",
       "35                /gpaw_0_95        None  /home/jovyan/thermo/encut_310/   \n",
       "36  /gpaw_0_9666666666666667        None  /home/jovyan/thermo/encut_310/   \n",
       "37  /gpaw_0_9833333333333333        None  /home/jovyan/thermo/encut_310/   \n",
       "38                 /gpaw_1_0        None  /home/jovyan/thermo/encut_310/   \n",
       "39  /gpaw_1_0166666666666666        None  /home/jovyan/thermo/encut_310/   \n",
       "40  /gpaw_1_0333333333333334        None  /home/jovyan/thermo/encut_310/   \n",
       "41                /gpaw_1_05        None  /home/jovyan/thermo/encut_310/   \n",
       "42                /gpaw_0_95        None  /home/jovyan/thermo/encut_320/   \n",
       "43  /gpaw_0_9666666666666667        None  /home/jovyan/thermo/encut_320/   \n",
       "44  /gpaw_0_9833333333333333        None  /home/jovyan/thermo/encut_320/   \n",
       "45                 /gpaw_1_0        None  /home/jovyan/thermo/encut_320/   \n",
       "46  /gpaw_1_0166666666666666        None  /home/jovyan/thermo/encut_320/   \n",
       "47  /gpaw_1_0333333333333334        None  /home/jovyan/thermo/encut_320/   \n",
       "48                /gpaw_1_05        None  /home/jovyan/thermo/encut_320/   \n",
       "\n",
       "                    timestart timestop totalcputime  \\\n",
       "0  2020-10-02 17:24:24.200176     None         None   \n",
       "1  2020-10-02 17:26:45.417210     None         None   \n",
       "2  2020-10-02 17:28:37.112334     None         None   \n",
       "3  2020-10-02 17:30:26.714705     None         None   \n",
       "4  2020-10-02 17:31:58.800251     None         None   \n",
       "5  2020-10-02 17:34:47.304029     None         None   \n",
       "6  2020-10-02 17:36:23.322563     None         None   \n",
       "7  2020-10-02 17:38:00.805999     None         None   \n",
       "8  2020-10-02 17:40:27.023982     None         None   \n",
       "9  2020-10-02 17:42:55.820191     None         None   \n",
       "10 2020-10-02 17:45:10.442772     None         None   \n",
       "11 2020-10-02 17:47:59.450726     None         None   \n",
       "12 2020-10-02 17:51:07.518608     None         None   \n",
       "13 2020-10-02 17:54:45.224784     None         None   \n",
       "14 2020-10-02 17:58:11.528057     None         None   \n",
       "15 2020-10-02 18:00:11.919363     None         None   \n",
       "16 2020-10-02 18:02:25.229474     None         None   \n",
       "17 2020-10-02 18:05:13.598633     None         None   \n",
       "18 2020-10-02 18:08:06.130672     None         None   \n",
       "19 2020-10-02 18:11:21.717226     None         None   \n",
       "20 2020-10-02 18:14:19.003564     None         None   \n",
       "21 2020-10-02 18:16:48.228097     None         None   \n",
       "22 2020-10-02 18:19:42.602848     None         None   \n",
       "23 2020-10-02 18:22:33.879253     None         None   \n",
       "24 2020-10-02 18:25:12.937586     None         None   \n",
       "25 2020-10-02 18:27:18.445423     None         None   \n",
       "26 2020-10-02 18:29:51.108935     None         None   \n",
       "27 2020-10-02 18:33:09.633165     None         None   \n",
       "28 2020-10-02 18:35:24.100573     None         None   \n",
       "29 2020-10-02 18:37:51.134146     None         None   \n",
       "30 2020-10-02 18:40:15.407176     None         None   \n",
       "31 2020-10-02 18:42:27.007123     None         None   \n",
       "32 2020-10-02 18:45:20.422390     None         None   \n",
       "33 2020-10-02 18:47:26.819490     None         None   \n",
       "34 2020-10-02 18:49:24.101232     None         None   \n",
       "35 2020-10-02 18:51:11.902579     None         None   \n",
       "36 2020-10-02 18:53:53.696423     None         None   \n",
       "37 2020-10-02 18:55:31.120613     None         None   \n",
       "38 2020-10-02 18:57:12.122217     None         None   \n",
       "39 2020-10-02 18:58:42.202686     None         None   \n",
       "40 2020-10-02 19:00:25.512077     None         None   \n",
       "41 2020-10-02 19:02:08.222775     None         None   \n",
       "42 2020-10-02 19:03:42.105391     None         None   \n",
       "43 2020-10-02 19:05:35.407201     None         None   \n",
       "44 2020-10-02 19:07:21.099215     None         None   \n",
       "45 2020-10-02 19:08:51.322535     None         None   \n",
       "46 2020-10-02 19:10:14.501550     None         None   \n",
       "47 2020-10-02 19:11:57.005028     None         None   \n",
       "48 2020-10-02 19:13:34.919295     None         None   \n",
       "\n",
       "                                       computer hamilton hamversion parentid  \\\n",
       "0   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "1   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "2   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "3   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "4   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "5   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "6   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "7   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "8   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "9   pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "10  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "11  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "12  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "13  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "14  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "15  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "16  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "17  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "18  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "19  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "20  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "21  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "22  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "23  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "24  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "25  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "26  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "27  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "28  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "29  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "30  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "31  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "32  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "33  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "34  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "35  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "36  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "37  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "38  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "39  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "40  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "41  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "42  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "43  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "44  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "45  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "46  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "47  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "48  pyiron@jupyter-pyiron-2dpyiron-2d996ovb6h#1  GpawJob       None     None   \n",
       "\n",
       "   masterid  \n",
       "0      None  \n",
       "1      None  \n",
       "2      None  \n",
       "3      None  \n",
       "4      None  \n",
       "5      None  \n",
       "6      None  \n",
       "7      None  \n",
       "8      None  \n",
       "9      None  \n",
       "10     None  \n",
       "11     None  \n",
       "12     None  \n",
       "13     None  \n",
       "14     None  \n",
       "15     None  \n",
       "16     None  \n",
       "17     None  \n",
       "18     None  \n",
       "19     None  \n",
       "20     None  \n",
       "21     None  \n",
       "22     None  \n",
       "23     None  \n",
       "24     None  \n",
       "25     None  \n",
       "26     None  \n",
       "27     None  \n",
       "28     None  \n",
       "29     None  \n",
       "30     None  \n",
       "31     None  \n",
       "32     None  \n",
       "33     None  \n",
       "34     None  \n",
       "35     None  \n",
       "36     None  \n",
       "37     None  \n",
       "38     None  \n",
       "39     None  \n",
       "40     None  \n",
       "41     None  \n",
       "42     None  \n",
       "43     None  \n",
       "44     None  \n",
       "45     None  \n",
       "46     None  \n",
       "47     None  \n",
       "48     None  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.job_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:39.529725Z",
     "start_time": "2019-09-04T12:58:38.466857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn was saved and received the ID: 50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/ase/cell.py:17: FutureWarning: Cell object will no longer have pbc\n",
      "  warnings.warn(deprecation_msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job strain_0_95 was saved and received the ID: 51\n",
      "The job strain_0_9666667 was saved and received the ID: 52\n",
      "The job strain_0_9833333 was saved and received the ID: 53\n",
      "The job strain_1_0 was saved and received the ID: 54\n",
      "The job strain_1_0166667 was saved and received the ID: 55\n",
      "The job strain_1_0333333 was saved and received the ID: 56\n",
      "The job strain_1_05 was saved and received the ID: 57\n",
      "job_id:  51 finished\n",
      "job_id:  52 finished\n",
      "job_id:  53 finished\n",
      "job_id:  54 finished\n",
      "job_id:  55 finished\n",
      "job_id:  56 finished\n",
      "job_id:  57 finished\n"
     ]
    }
   ],
   "source": [
    "murn.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afterwards we can use the build in capabilites to plot the resulting energy volume curve and fit different equations of state to the calculated energy volume pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:39.624624Z",
     "start_time": "2019-09-04T12:59:39.574891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>volume</th>\n",
       "      <th>energy</th>\n",
       "      <th>error</th>\n",
       "      <th>id</th>\n",
       "      <th>equilibrium_b_prime</th>\n",
       "      <th>equilibrium_bulk_modulus</th>\n",
       "      <th>equilibrium_energy</th>\n",
       "      <th>equilibrium_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.757031</td>\n",
       "      <td>-16.527632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51</td>\n",
       "      <td>4.704621</td>\n",
       "      <td>280.897787</td>\n",
       "      <td>-16.623369</td>\n",
       "      <td>21.189923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.103646</td>\n",
       "      <td>-16.569446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52</td>\n",
       "      <td>4.704621</td>\n",
       "      <td>280.897787</td>\n",
       "      <td>-16.623369</td>\n",
       "      <td>21.189923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.450260</td>\n",
       "      <td>-16.599599</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>4.704621</td>\n",
       "      <td>280.897787</td>\n",
       "      <td>-16.623369</td>\n",
       "      <td>21.189923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.796875</td>\n",
       "      <td>-16.616336</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>4.704621</td>\n",
       "      <td>280.897787</td>\n",
       "      <td>-16.623369</td>\n",
       "      <td>21.189923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.143490</td>\n",
       "      <td>-16.623997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55</td>\n",
       "      <td>4.704621</td>\n",
       "      <td>280.897787</td>\n",
       "      <td>-16.623369</td>\n",
       "      <td>21.189923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.490104</td>\n",
       "      <td>-16.619111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56</td>\n",
       "      <td>4.704621</td>\n",
       "      <td>280.897787</td>\n",
       "      <td>-16.623369</td>\n",
       "      <td>21.189923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21.836719</td>\n",
       "      <td>-16.607260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57</td>\n",
       "      <td>4.704621</td>\n",
       "      <td>280.897787</td>\n",
       "      <td>-16.623369</td>\n",
       "      <td>21.189923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      volume     energy  error  id  equilibrium_b_prime  \\\n",
       "0  19.757031 -16.527632    0.0  51             4.704621   \n",
       "1  20.103646 -16.569446    0.0  52             4.704621   \n",
       "2  20.450260 -16.599599    0.0  53             4.704621   \n",
       "3  20.796875 -16.616336    0.0  54             4.704621   \n",
       "4  21.143490 -16.623997    0.0  55             4.704621   \n",
       "5  21.490104 -16.619111    0.0  56             4.704621   \n",
       "6  21.836719 -16.607260    0.0  57             4.704621   \n",
       "\n",
       "   equilibrium_bulk_modulus  equilibrium_energy  equilibrium_volume  \n",
       "0                280.897787          -16.623369           21.189923  \n",
       "1                280.897787          -16.623369           21.189923  \n",
       "2                280.897787          -16.623369           21.189923  \n",
       "3                280.897787          -16.623369           21.189923  \n",
       "4                280.897787          -16.623369           21.189923  \n",
       "5                280.897787          -16.623369           21.189923  \n",
       "6                280.897787          -16.623369           21.189923  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "murn.output_to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:39.850020Z",
     "start_time": "2019-09-04T12:59:39.628014Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "murn.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:39.919616Z",
     "start_time": "2019-09-04T12:59:39.851944Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_type': 'vinet',\n",
       " 'volume_eq': 21.19185363600345,\n",
       " 'energy_eq': -16.623384845899427,\n",
       " 'bulkmodul_eq': 280.93805771100557,\n",
       " 'b_prime_eq': 4.105492272090299,\n",
       " 'least_square_error': array([4.97985872e-04, 1.01905536e+01, 1.63735940e+00, 8.58104678e-03])}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "murn.fit_vinet()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common mistakes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Not copying the basis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to copy the basis before applying the strain, as the strain has to be applied on the initial structure, not the previous structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:39.960916Z",
     "start_time": "2019-09-04T12:59:39.921187Z"
    }
   },
   "outputs": [],
   "source": [
    "volume_lst_with_copy = []\n",
    "for strain in np.linspace(0.95, 1.05, 7):\n",
    "    basis_copy = basis.copy()\n",
    "    basis_copy.set_cell(cell=basis.cell * strain ** (1/3), scale_atoms=True)\n",
    "    volume_lst_with_copy.append(basis_copy.get_volume())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:39.972769Z",
     "start_time": "2019-09-04T12:59:39.962410Z"
    }
   },
   "outputs": [],
   "source": [
    "basis_copy = basis.copy()\n",
    "volume_lst_without_copy = []\n",
    "for strain in np.linspace(0.95, 1.05, 7):\n",
    "    basis_copy.set_cell(cell=basis_copy.cell * strain ** (1/3), scale_atoms=True)\n",
    "    volume_lst_without_copy.append(basis_copy.get_volume())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:39.998610Z",
     "start_time": "2019-09-04T12:59:39.974290Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([19.757031250000004,\n",
       "  20.10364583333333,\n",
       "  20.450260416666666,\n",
       "  20.796874999999996,\n",
       "  21.143489583333338,\n",
       "  21.490104166666654,\n",
       "  21.83671875000001],\n",
       " [19.757031250000004,\n",
       "  19.098463541666664,\n",
       "  18.780155815972222,\n",
       "  18.780155815972222,\n",
       "  19.09315841290509,\n",
       "  19.729597026668593,\n",
       "  20.716076878002024])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume_lst_with_copy, volume_lst_without_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rescaling the cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common issue is the rescaling of the supercell, there are multiple options to choose from. We used the option to scale the atoms with the supercell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:40.083034Z",
     "start_time": "2019-09-04T12:59:40.000103Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6ed16a7f03a4134ba944169b0266942",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basis_copy = basis.copy()\n",
    "strain = 0.5\n",
    "basis_copy.set_cell(cell=basis_copy.cell * strain ** (1/3), scale_atoms=True)\n",
    "basis_copy.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A nother typical case is rescaling the cell to increase the distance between the atoms or add vacuum. But that is not what we want to fit an energy volume curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:40.152204Z",
     "start_time": "2019-09-04T12:59:40.084706Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36e3b7cd9ff84541836325e8c24d6a1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basis_copy = basis.copy()\n",
    "strain = 0.5\n",
    "basis_copy.set_cell(cell=basis_copy.cell * strain ** (1/3), scale_atoms=False)\n",
    "basis_copy.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-16T19:33:52.089315Z",
     "start_time": "2018-10-16T19:33:52.085882Z"
    }
   },
   "source": [
    "The same can be achieved by setting the basis to relative coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:40.221411Z",
     "start_time": "2019-09-04T12:59:40.153745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b3d11b6788c41bfabb662dbd6573f2e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basis_copy = basis.copy()\n",
    "strain = 0.5\n",
    "basis_copy.set_relative()\n",
    "basis_copy.cell *= strain ** (1/3)\n",
    "basis_copy.plot3d()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-04T12:59:40.287442Z",
     "start_time": "2019-09-04T12:59:40.222918Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8aaa6bdcf84046f09e9e855fb0c3c173",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basis_copy = basis.copy()\n",
    "strain = 0.5\n",
    "basis_copy.cell *= strain ** (1/3)\n",
    "basis_copy.plot3d()"
   ]
  },
  {
   "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": "3.7.8"
  },
  "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": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}