part_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "297aedc2-810f-4b78-bd4c-e29c54793585",
   "metadata": {},
   "source": [
    "# Calculating Materials Properties with pyiron"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd367e20-9a5c-4b5d-9f86-28735f4fc14d",
   "metadata": {},
   "source": [
    "Author: [jan-janssen](https://jan-janssen.com)\n",
    "\n",
    "When working with interatomic potentials, it is commonly difficult to assess the quality of a new interatomic potential. pyiron can accelerate this assessment by providing simulation protocols to calculate pre-defined materials properties. In this example we demonstrate the calculation of the elastic constants and the band structure. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbda2c7-61bd-4791-94e8-2dcfbf934aaf",
   "metadata": {},
   "source": [
    "## Calculate Elastic Constants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0564cc91-168b-479d-a840-db2bfca4f6fb",
   "metadata": {},
   "source": [
    "Rather than reimplementing its own method to calculate the elastic constants, pyiron relies on existing codes from the community and primarily provides and interface to connect these codes. For calculating the elastic constants we use the [ElaStic](https://doi.org/10.1016/j.cpc.2013.03.010) code. As this code is published under the General Public License (GPL) we load an additional `pyiron_gpl` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b7d6d57-4000-4b5f-a1c7-5401fc6a15b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyiron_gpl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1422c08e-4412-40d5-884a-bf8a45000bd1",
   "metadata": {},
   "source": [
    "Afterwards, just like before the `Project` class is imported from the `pyiron_atomistics` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95d02495-50cf-4444-b64e-fcc37d74e132",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron_atomistics import Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed39b17-15d6-4ccf-81e3-e424e8f11cb9",
   "metadata": {},
   "source": [
    "We then create a `Project` named `elastic` for the calculation of the elastic matrix and again we remove all existing calculation in this project to have a fresh start. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dfb03ab2-63bd-4d8d-a460-181781afdc1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = Project(\"materials\")\n",
    "pr.remove_jobs(silently=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b0c817-dd2a-4e9f-85e8-e90f540c697e",
   "metadata": {},
   "source": [
    "Following the same steps as in the previous tutorial on calculating energy volume curves, we again create a reference LAMMPS job to specify the atomistic structure and the interatomic potential. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3aa4dc92-0ae6-4738-b40f-ffa883ff756d",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_lmp = pr.create.job.Lammps(job_name=\"lmp\")\n",
    "job_lmp.structure = pr.create.structure.ase.bulk(name=\"Al\", cubic=True)\n",
    "job_lmp.potential = '1999--Mishin-Y--Al--LAMMPS--ipr1'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b74f6dd-746b-4e55-96bf-56dadfe89417",
   "metadata": {},
   "source": [
    "The reference job is assinged to the `ElasticMatrixJob` afterwards. This `ElasticMatrixJob` is again an `GenericMaster` job which does not interface to a specific simulation code but rather coordinates the execution of multiple individual calculation to calculate a combined property, in this case the elastic constants. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "228743ad-fc8f-46a9-b6fc-d33eb31313e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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_of_points</td>\n",
       "      <td>5</td>\n",
       "      <td>number of sample point per deformation directions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fit_order</td>\n",
       "      <td>2</td>\n",
       "      <td>order of the fit polynom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eps_range</td>\n",
       "      <td>0.005</td>\n",
       "      <td>strain variation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>relax_atoms</td>\n",
       "      <td>True</td>\n",
       "      <td>relax atoms in deformed structure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sqrt_eta</td>\n",
       "      <td>True</td>\n",
       "      <td>calculate self-consistently sqrt of stress matrix eta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Parameter  Value                                                Comment\n",
       "0  num_of_points      5      number of sample point per deformation directions\n",
       "1      fit_order      2                               order of the fit polynom\n",
       "2      eps_range  0.005                                       strain variation\n",
       "3    relax_atoms   True                      relax atoms in deformed structure\n",
       "4       sqrt_eta   True  calculate self-consistently sqrt of stress matrix eta"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_elastic = pr.create.job.ElasticMatrixJob(job_name=\"elastic\")\n",
    "job_elastic.ref_job = job_lmp\n",
    "job_elastic.input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cbc06c6-83fe-4b55-94bd-9e6932e29a2a",
   "metadata": {},
   "source": [
    "The `ElasticMatrixJob` can then be executed by calling the `run()` method. Once again by leveraging the same abstract job class for individual calculation like the LAMMPS job object as well as `GenericMaster` jobs like the `ElasticMatrixJob` both jobs can be distributed to queuing systems and so on. Here the job is just executed in-line with the python process of the jupyter notebook, which is called `modal` execution in pyiron.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4057a2d8-4318-469b-9bdc-0730c04fe95b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job elastic was saved and received the ID: 21797\n",
      "The job s_e_0 was saved and received the ID: 21798\n",
      "The job s_01_e_m0_00500 was saved and received the ID: 21799\n",
      "The job s_01_e_m0_00250 was saved and received the ID: 21800\n",
      "The job s_01_e_0_00250 was saved and received the ID: 21801\n",
      "The job s_01_e_0_00500 was saved and received the ID: 21802\n",
      "The job s_08_e_m0_00500 was saved and received the ID: 21803\n",
      "The job s_08_e_m0_00250 was saved and received the ID: 21804\n",
      "The job s_08_e_0_00250 was saved and received the ID: 21805\n",
      "The job s_08_e_0_00500 was saved and received the ID: 21806\n",
      "The job s_23_e_m0_00500 was saved and received the ID: 21807\n",
      "The job s_23_e_m0_00250 was saved and received the ID: 21808\n",
      "The job s_23_e_0_00250 was saved and received the ID: 21809\n",
      "The job s_23_e_0_00500 was saved and received the ID: 21810\n"
     ]
    }
   ],
   "source": [
    "job_elastic.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d3297e-b13a-41bd-81b1-7c9e959c2406",
   "metadata": {},
   "source": [
    "After the successful execution the elastic matrix can be loaded from the output: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2bd4ba0-47c3-4c0f-9f41-f11cef26d1db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[114.10393026,  60.51098917,  60.51098917,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [ 60.51098917, 114.10393026,  60.51098917,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [ 60.51098917,  60.51098917, 114.10393026,   0.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,  31.67539615,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         31.67539615,   0.        ],\n",
       "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,  31.67539615]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_elastic[\"output/elasticmatrix\"][\"C\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1282ecc5-15db-4fc3-8744-12df06f9e0e9",
   "metadata": {},
   "source": [
    "## Calculate the band structure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e918597-b15f-4b32-84bd-f9814f96df0c",
   "metadata": {},
   "source": [
    "In analogy to the elastic constants above pyiron also interfaces with the [phonopy](https://phonopy.github.io/phonopy/index.html) package to calculate the bandstructure and total density of states for a given interatomic potential. The great advantage of using these tools via pyiron rather than using them directly is that the interface to set the reference job object to calculate the energies and forces for the particular materials property is always the same. So once again we define a LAMMPS reference job which is used to calculate the bandstructure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4cc077bd-659b-436f-bf1d-87b5bd732a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_lmp = pr.create.job.Lammps(job_name=\"lmp\")\n",
    "job_lmp.structure = pr.create.structure.ase.bulk(name=\"Al\", cubic=True)\n",
    "job_lmp.potential = '1999--Mishin-Y--Al--LAMMPS--ipr1'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaaf66bf-7d10-4049-9388-805bcc719ed9",
   "metadata": {},
   "source": [
    "The reference job is afterwards assigned to the `PhonopyJob` which itself is again a `GenericMaster` job:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40a940c8-614a-49c4-9689-d8fbe05f4aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</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>interaction_range</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>Minimal size of supercell, Ang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>factor</td>\n",
       "      <td>15.633302</td>\n",
       "      <td>Frequency unit conversion factor (default for VASP)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>displacement</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>atoms displacement, Ang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dos_mesh</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>mesh size for DOS calculation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>primitive_matrix</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>number_of_snapshots</td>\n",
       "      <td>NaN</td>\n",
       "      <td>int or None, optional. Number of snapshots of supercells with random displacements. Random displacements are generated displacing all atoms in random directions with a fixed displacement distance ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Parameter      Value  \\\n",
       "0    interaction_range  10.000000   \n",
       "1               factor  15.633302   \n",
       "2         displacement   0.010000   \n",
       "3             dos_mesh  20.000000   \n",
       "4     primitive_matrix        NaN   \n",
       "5  number_of_snapshots        NaN   \n",
       "\n",
       "                                                                                                                                                                                                   Comment  \n",
       "0                                                                                                                                                                           Minimal size of supercell, Ang  \n",
       "1                                                                                                                                                      Frequency unit conversion factor (default for VASP)  \n",
       "2                                                                                                                                                                                  atoms displacement, Ang  \n",
       "3                                                                                                                                                                            mesh size for DOS calculation  \n",
       "4                                                                                                                                                                                                           \n",
       "5  int or None, optional. Number of snapshots of supercells with random displacements. Random displacements are generated displacing all atoms in random directions with a fixed displacement distance ...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_phonon = pr.create.job.PhonopyJob(job_name=\"phonon\")\n",
    "job_phonon.ref_job = job_lmp\n",
    "job_phonon.input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88533dd5-9b68-4a76-bd25-db8639db6683",
   "metadata": {},
   "source": [
    "The `PhonopyJob` is executed by calling the run method: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfe35176-3bf6-4603-bb3d-ec195c4a65b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job phonon was saved and received the ID: 21811\n",
      "The job lmp_0 was saved and received the ID: 21812\n"
     ]
    }
   ],
   "source": [
    "job_phonon.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf2322fe-b718-4f87-8535-5f72291536ea",
   "metadata": {},
   "source": [
    "Finally, just like we did for the energy volume curve calculation in the previous tutorial, we can use the internal plot functions of the `PhonopyJob` to plot the band structure and the total density of states. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e85c1ca-9211-42fa-b8ac-6223ce14e500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Bandstructure'}, xlabel='Bandpath', ylabel='Frequency [THz]'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job_phonon.plot_band_structure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d4d29ced-9a22-46f4-a10d-c184c23c0f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job_phonon.plot_dos()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76fb6a24-01b3-4276-b7b5-b7828e525b0c",
   "metadata": {},
   "source": [
    "To go beyond the harmonic approximation and include the volume expansion in terms of the quasi-harmonic approximation, we can use the `QuasiHarmonicJob` class which executes a `PhonopyJob` for various volumes and provides the utility functions to calculate the free energy in the resulting quasi-harmonic approximation. This functionality is explained in more detail in the [virtual course](https://pyiron.org/phasediagram-workshop-2020/ThermodynamicProperties.html) on the pyiron website. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe4b1cc5-caf6-492b-8f85-8b40c704d26f",
   "metadata": {},
   "source": [
    "## Fitting machine learning interatomic potentials"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e31a467-c622-4cd9-ba39-79318f4abe18",
   "metadata": {},
   "source": [
    "The fitting of machine learning interatomic potentials requires a separate tutorial, so it is not included in this presentation. Instead it was discussed in the [Workshop: From Electrons to Phase Diagrams](http://potentials.rub.de/2022/index.php). The recordings of this workshop as well as the interactive jupyter notebooks are [available online](http://pyiron.org/potentials-workshop-2022/intro.html). We plan to extend this interface and in particular the [FitSNAP](https://github.com/FitSNAP) package. So if these topics are interesting to you feel free to reach out on Github or contact the pyiron developers directly. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81be9aae-adb4-451a-aa95-a8ad64222052",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf5d844-a814-4b4b-a804-fb868caeac5f",
   "metadata": {},
   "source": [
    "Beyond the ability to rapid-prototype and up-scale your own simulation protocols, pyiron already provides interfaces to a series of material property calculators and continues to expand this functionality, with the goal to make atomistic calculations more accessible to a larger audience. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82ffc914-cbfb-4c27-8716-987eef133994",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "flux",
   "language": "python",
   "name": "flux"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}