part_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b90b901f-a811-4abb-afcd-8738cb0e401c",
   "metadata": {},
   "source": [
    "# Beyond a single LAMMPS calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caa55036-9716-47a5-94b6-fd50cc5148aa",
   "metadata": {},
   "source": [
    "Author: [jan-janssen](https://jan-janssen.com)\n",
    "\n",
    "While pyiron can simplify the submission an analysis of individual calculation, for example in a lecture setting, the primary benefit of using pyiron is the ability to combine both the rapid prototyping of a simulation protocol as well as the up-scaling of the simulation protocol. So going beyond the previous example this, tutorial covers orchestrating a series of individual calculation and how pyiron can leverage advanced LAMMPS features like the Python library to accelerate your parameter studies. \n",
    "\n",
    "As an example we are going to calculate the energy volume dependence. This is typically used in ab-initio calculation to determine the equilibrium volume, equilibrium bulk modulus and its derivative. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ee438a-2337-4bce-b9c5-5a9f3aaeae36",
   "metadata": {},
   "source": [
    "# Start with an empty Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "893f3b62-0807-45e8-9966-5e59b0984588",
   "metadata": {},
   "source": [
    "In analogy to the first tutorial we again start by importing the `Project` class from the `pyiron_atomistics` module:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85273524-b9f0-4ed9-874b-14e40551eb31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8250125d31c44689d69ab486da9f9a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyiron_atomistics import Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a7ffd3-ff74-40e1-bd4a-87a2ecf77631",
   "metadata": {},
   "source": [
    "Afterwards we create a new `Project` instance for the following calculation named `evcurve` short for energy volume curve. We delete all previous calculation in this project, just to make sure everything is recalculated every time the jupyter notebook is executed rather than reloading the calculation: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de4e9eb5-23fc-48a8-b92e-2b120c2e4bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = Project(path=\"evcurve\")\n",
    "pr.remove_jobs(silently=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10b1e5cc-0259-4259-bbb8-39fbf5d11cbf",
   "metadata": {},
   "source": [
    "# Loop over different strain values\n",
    "To measure the equilibrium volume, equilibrium bulk modulus and its derivative, we calculate the dependence of the energy on the volume. To change the volume we apply strains ranging from $-5\\%$ to $+5\\%$. Afterwards we fit the resulting energy volume dependence with a ploynomial. The minimum of this polynomial is the equilibrium volume, the second derivative is the bulk modulus and the third derivative corresponds to the pressure derivative of the bulk modulus. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4054e193-d34a-4ab4-867e-9fc73441c608",
   "metadata": {},
   "source": [
    "## Create a template job"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589c3894-3c0b-471f-bf52-166cc9937889",
   "metadata": {},
   "source": [
    "As a first step a template job object is created. Here we keep the default settings for the LAMMPS job object which corresponds to a static calculation `run 0`. We only change the structure to apply the strain and the interatomic potential. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e109d2e1-73c5-4656-9941-1b8502272a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_template = pr.create.job.Lammps(job_name=\"lmp\")\n",
    "structure_template = pr.create.structure.ase.bulk(name=\"Al\", cubic=True)\n",
    "job_template.structure = structure_template"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5793576e-4b38-44a1-94c7-b412fa4b43ac",
   "metadata": {},
   "source": [
    "To choose an interatomic potential we again use the `list_potential()` function applied on the template job object. As the aluminium structure was already assigned to the job object, the `list_potential()` function only returns interatomic potentials which include interactions for aluminium:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a3bba12-3b4f-453c-bef0-640177edbd5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1',\n",
       " '1996--Farkas-D--Nb-Ti-Al--LAMMPS--ipr1',\n",
       " '1997--Liu-X-Y--Al-Mg--LAMMPS--ipr1',\n",
       " '1998--Liu-X-Y--Al-Mg--LAMMPS--ipr1',\n",
       " '1999--Liu-X-Y--Al-Cu--LAMMPS--ipr1',\n",
       " '1999--Mishin-Y--Al--LAMMPS--ipr1',\n",
       " '2000--Landa-A--Al-Pb--LAMMPS--ipr1',\n",
       " '2000--Sturgeon-J-B--Al--LAMMPS--ipr1',\n",
       " '2002--Mishin-Y--Ni-Al--LAMMPS--ipr1',\n",
       " '2003--Lee-B-J--Al--LAMMPS--ipr1',\n",
       " '2003--Zope-R-R--Al--LAMMPS--ipr1',\n",
       " '2003--Zope-R-R--Ti-Al--LAMMPS--ipr1',\n",
       " '2004--Liu-X-Y--Al--LAMMPS--ipr1',\n",
       " '2004--Mishin-Y--Ni-Al--LAMMPS--ipr1',\n",
       " '2004--Mishin-Y--Ni-Al--LAMMPS--ipr2',\n",
       " '2004--Zhou-X-W--Al--LAMMPS--ipr2',\n",
       " '2005--Mendelev-M-I--Al-Fe--LAMMPS--ipr1',\n",
       " '2007--Silva-A-C--Al-Ni--LAMMPS--ipr1',\n",
       " '2008--Mendelev-M-I--Al--LAMMPS--ipr1',\n",
       " '2009--Kim-Y-M--Mg-Al--LAMMPS--ipr1',\n",
       " '2009--Mendelev-M-I--Al-Mg--LAMMPS--ipr1',\n",
       " '2009--Purja-Pun-G-P--Ni-Al--LAMMPS--ipr1',\n",
       " '2009--Zhakhovskii-V-V--Al--LAMMPS--ipr2',\n",
       " '2010--Lee-E--Fe-Al--LAMMPS--ipr1',\n",
       " '2010--Mendelev-M-I--fictional-Al-1--LAMMPS--ipr1',\n",
       " '2010--Mendelev-M-I--fictional-Al-2--LAMMPS--ipr1',\n",
       " '2010--Mendelev-M-I--fictional-Al-3--LAMMPS--ipr1',\n",
       " '2010--Winey-J-M--Al--LAMMPS--ipr1',\n",
       " '2011--Apostol-F--Al-Cu--LAMMPS--ipr1',\n",
       " '2011--Ko-W-S--Al-H--LAMMPS--ipr1',\n",
       " '2012--Dong-W-P--Co-Al--LAMMPS--ipr1',\n",
       " '2012--Jelinek-B--Al-Si-Mg-Cu-Fe--LAMMPS--ipr2',\n",
       " '2012--Schopf-D--Al-Mn-Pd--LAMMPS--ipr1',\n",
       " '2013--Shim-J-H--V-Al--LAMMPS--ipr1',\n",
       " '2013--Shim-J-H--V-Al-H--LAMMPS--ipr1',\n",
       " '2015--Choudhary-K--Al--LAMMPS--ipr1',\n",
       " '2015--Choudhary-K--Al-O--LAMMPS--ipr1',\n",
       " '2015--Kim-Y-K--Ni-Al-Co--LAMMPS--ipr1',\n",
       " '2015--Kumar-A--Al-Ni--LAMMPS--ipr1',\n",
       " '2015--Kumar-A--Al-Ni-O--LAMMPS--ipr1',\n",
       " '2015--Mendelev-M-I--Al-Sm--LAMMPS--ipr1',\n",
       " '2015--Pascuet-M-I--Al--LAMMPS--ipr1',\n",
       " '2015--Pascuet-M-I--Al-U--LAMMPS--ipr2',\n",
       " '2015--Purja-Pun-G-P--Al-Co--LAMMPS--ipr2',\n",
       " '2015--Purja-Pun-G-P--Ni-Al-Co--LAMMPS--ipr2',\n",
       " '2016--Kim-Y-K--Al-Ti--LAMMPS--ipr1',\n",
       " '2016--Zhou-X-W--Al-Cu--LAMMPS--ipr2',\n",
       " '2017--Botu-V--Al--LAMMPS--ipr1',\n",
       " '2017--Kim-J-S--Pt-Al--LAMMPS--ipr1',\n",
       " '2017--Kim-Y-K--Ni-Al-Ti--LAMMPS--ipr1',\n",
       " '2018--Dickel-D-E--Mg-Al-Zn--LAMMPS--ipr1',\n",
       " '2018--Jeong-G-U--Pd-Al--LAMMPS--ipr1',\n",
       " '2018--Zhou-X-W--Al-Cu-H--LAMMPS--ipr1',\n",
       " '2019--Plummer-G--Ti-Al-C--LAMMPS--ipr1',\n",
       " '2020--Farkas-D--Fe-Ni-Cr-Co-Al--LAMMPS--ipr1',\n",
       " '2020--Purja-Pun-G-P--Al--LAMMPS--ipr1',\n",
       " '2020--Starikov-S--Si-Au-Al--LAMMPS--ipr1',\n",
       " '2020--Starikov-S--Si-Au-Al--LAMMPS--ipr2',\n",
       " '2021--Plummer-G--Ti-Al-C--LAMMPS--ipr1',\n",
       " '2021--Song-H--Al-Sm--LAMMPS--ipr1',\n",
       " '2022--Fereidonnejad-R--Al-Hf--LAMMPS--ipr1',\n",
       " '2022--Fereidonnejad-R--Al-Nb--LAMMPS--ipr1',\n",
       " '2022--Fereidonnejad-R--Al-Ta--LAMMPS--ipr1',\n",
       " '2022--Fereidonnejad-R--Al-Ti--LAMMPS--ipr1',\n",
       " '2022--Fereidonnejad-R--Al-Zr--LAMMPS--ipr1',\n",
       " '2022--Mahata-A--Al-Cu--LAMMPS--ipr1',\n",
       " '2022--Mahata-A--Al-Fe--LAMMPS--ipr1',\n",
       " '2022--Mahata-A--Al-Ni--LAMMPS--ipr1',\n",
       " '2022--Mendelev-M-I--Ni-Al--LAMMPS--ipr1',\n",
       " 'EAM_CubicNaturalSpline_ErcolessiAdams_1994_Al__MO_800509458712_002',\n",
       " 'EAM_Dynamo_AngeloMoodyBaskes_1995_NiAlH__MO_418978237058_005',\n",
       " 'EAM_Dynamo_CaiYe_1996_AlCu__MO_942551040047_005',\n",
       " 'EAM_Dynamo_ErcolessiAdams_1994_Al__MO_123629422045_005',\n",
       " 'EAM_Dynamo_FarkasJones_1996_NbTiAl__MO_042691367780_000',\n",
       " 'EAM_Dynamo_JacobsenNorskovPuska_1987_Al__MO_411692133366_000',\n",
       " 'EAM_Dynamo_LandaWynblattSiegel_2000_AlPb__MO_699137396381_005',\n",
       " 'EAM_Dynamo_LiuAdams_1998_AlMg__MO_019873715786_000',\n",
       " 'EAM_Dynamo_LiuErcolessiAdams_2004_Al__MO_051157671505_000',\n",
       " 'EAM_Dynamo_LiuLiuBorucki_1999_AlCu__MO_020851069572_000',\n",
       " 'EAM_Dynamo_LiuOhotnickyAdams_1997_AlMg__MO_559870613549_000',\n",
       " 'EAM_Dynamo_MendelevAstaRahman_2009_AlMg__MO_658278549784_005',\n",
       " 'EAM_Dynamo_MendelevFangYe_2015_AlSm__MO_338600200739_000',\n",
       " 'EAM_Dynamo_MendelevKramerBecker_2008_Al__MO_106969701023_005',\n",
       " 'EAM_Dynamo_MendelevSrolovitzAckland_2005_AlFe__MO_577453891941_005',\n",
       " 'EAM_Dynamo_MishinFarkasMehl_1999_Al__MO_651801486679_005',\n",
       " 'EAM_Dynamo_MishinMehlPapaconstantopoulos_2002_NiAl__MO_109933561507_005',\n",
       " 'EAM_Dynamo_Mishin_2004_NiAl__MO_101214310689_005',\n",
       " 'EAM_Dynamo_PunMishin_2009_NiAl__MO_751354403791_005',\n",
       " 'EAM_Dynamo_PunYamakovMishin_2013_AlCo__MO_678952612413_000',\n",
       " 'EAM_Dynamo_PunYamakovMishin_2013_NiAlCo__MO_826591359508_000',\n",
       " 'EAM_Dynamo_SchopfBrommerFrigan_2012_AlMnPd__MO_137572817842_000',\n",
       " 'EAM_Dynamo_SturgeonLaird_2000_Al__MO_120808805541_005',\n",
       " 'EAM_Dynamo_VailheFarkas_1997_CoAl__MO_284963179498_005',\n",
       " 'EAM_Dynamo_WineyKubotaGupta_2010_Al__MO_149316865608_005',\n",
       " 'EAM_Dynamo_Zhakhovsky_2009_Al__MO_519613893196_000',\n",
       " 'EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Al__MO_060567868558_000',\n",
       " 'EAM_Dynamo_ZhouJohnsonWadley_2004_Al__MO_131650261510_005',\n",
       " 'EAM_Dynamo_ZhouWadleyJohnson_2001_Al__MO_049243498555_000',\n",
       " 'EAM_Dynamo_ZopeMishin_2003_Al__MO_664470114311_005',\n",
       " 'EAM_Dynamo_ZopeMishin_2003_TiAl__MO_117656786760_005',\n",
       " 'EAM_ErcolessiAdams_1994_Al__MO_324507536345_003',\n",
       " 'EAM_IMD_BrommerGaehler_2006A_AlNiCo__MO_122703700223_003',\n",
       " 'EAM_IMD_BrommerGaehler_2006B_AlNiCo__MO_128037485276_003',\n",
       " 'EAM_IMD_SchopfBrommerFrigan_2012_AlMnPd__MO_878712978062_003',\n",
       " 'EAM_QuinticClampedSpline_ErcolessiAdams_1994_Al__MO_450093727396_002',\n",
       " 'EAM_QuinticHermiteSpline_ErcolessiAdams_1994_Al__MO_781138671863_002',\n",
       " 'EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_AlAgAuCuNiPdPt__MO_115316750986_001',\n",
       " 'EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Al__MO_623376124862_001',\n",
       " 'MEAM_LAMMPS_AlmyrasSangiovanniSarakinos_2019_NAlTi__MO_958395190627_000',\n",
       " 'MEAM_LAMMPS_CostaAgrenClavaguera_2007_AlNi__MO_131642768288_000',\n",
       " 'MEAM_LAMMPS_DongKimKo_2012_CoAl__MO_099716416216_000',\n",
       " 'MEAM_LAMMPS_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__MO_262519520678_000',\n",
       " 'MEAM_LAMMPS_JeongParkDo_2018_PdAl__MO_616482358807_000',\n",
       " 'MEAM_LAMMPS_KimJungLee_2015_NiAlCo__MO_876687166519_000',\n",
       " 'MEAM_LAMMPS_KimKimJung_2016_AlTi__MO_618133763375_000',\n",
       " 'MEAM_LAMMPS_KimKimJung_2017_NiAlTi__MO_478967255435_000',\n",
       " 'MEAM_LAMMPS_KimKimLee_2009_AlMg__MO_058537087384_000',\n",
       " 'MEAM_LAMMPS_KimSeolJi_2017_PtAl__MO_793141037706_000',\n",
       " 'MEAM_LAMMPS_KoShimLee_2011_AlH__MO_127847080751_000',\n",
       " 'MEAM_LAMMPS_LeeLee_2010_FeAl__MO_332211522050_000',\n",
       " 'MEAM_LAMMPS_PascuetFernandez_2015_AlU__MO_596300673917_000',\n",
       " 'MEAM_LAMMPS_PascuetFernandez_2015_Al__MO_315820974149_000',\n",
       " 'MEAM_LAMMPS_ShimKoKim_2013_AlVH__MO_344724145339_000',\n",
       " 'Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Al__MO_140175748626_004',\n",
       " 'Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Al__MO_411898953661_004',\n",
       " 'Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Al__MO_279544746097_004',\n",
       " 'Sim_LAMMPS_ADP_ApostolMishin_2011_AlCu__SM_667696763561_000',\n",
       " 'Sim_LAMMPS_ADP_StarikovGordeevLysogorskiy_2020_SiAuAl__SM_113843830602_000',\n",
       " 'Sim_LAMMPS_AGNI_BotuBatraChapman_2017_Al__SM_666183636896_000',\n",
       " 'Sim_LAMMPS_BOP_ZhouWardFoster_2016_AlCu__SM_566399258279_000',\n",
       " 'Sim_LAMMPS_MEAM_AlmyrasSangiovanniSarakinos_2019_NAlTi__SM_871795249052_000',\n",
       " 'Sim_LAMMPS_MEAM_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__SM_656517352485_000',\n",
       " 'Sim_LAMMPS_MEAM_PascuetFernandez_2015_AlU__SM_721930391003_000',\n",
       " 'Sim_LAMMPS_MEAM_PascuetFernandez_2015_Al__SM_811588957187_000',\n",
       " 'Sim_LAMMPS_SMTBQ_SallesPolitanoAmzallag_2016_AlO__SM_853967355976_000',\n",
       " 'Sim_LAMMPS_SMTBQ_SallesPolitanoAmzallag_2016_Al__SM_404097633924_000']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_template.list_potentials()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcfe8850-627c-4cec-a880-5dad50af0a0b",
   "metadata": {},
   "source": [
    "As a first step we select one of the aluminium potentials, here we choose the potential from Yuri Mishin, but that is just a personal preference: [1999--Mishin-Y--Al--LAMMPS--ipr1](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.65.224114) More details about the potential are again available via the publication list generated at the end of the project. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f8f4ed62-9396-43a6-8777-82f1fb4c592a",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_template.potential = '1999--Mishin-Y--Al--LAMMPS--ipr1'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc489cd-1634-437e-b381-33b5bf82301e",
   "metadata": {},
   "source": [
    "In addition to the interatomic potential we also once again select the flux enabled executable as we are using the flux kernel. Still for now we execute all the calculation inline with the primary python process. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62f095e8-d6df-4021-ab79-719a4ff883ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_template.executable.version = '2022.11.03_flux'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca6c78d-bafa-4cac-bc0e-da817eb58985",
   "metadata": {},
   "source": [
    "## For loop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d269e2b-86aa-4700-af19-64d2b854fd7b",
   "metadata": {},
   "source": [
    "After defining the template job the next step is the execution of the for loop iterating over different strains. Here we import the numpy modul to generate an array of linearly spaced strains ranging from $-5\\%$ to $+5\\%$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c11a477-60a2-4fa8-bc01-cdbe8580bd38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6dfc28f-3090-405c-802b-6ed5819b6d5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "strain_lst = np.linspace(-0.05, 0.05, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab8629b-7659-4d3b-8056-a00925bf35b1",
   "metadata": {},
   "source": [
    "To iterate over the individual strains we have to copy both the LAMMPS job object, to create a new LAMMPS calculation for each strain as well as the structure object to gurantee that we always start from the equilibrium structure when we apply the selected strain. For the structure object we can use the simple `copy()` function. In contrast for copying the job object we use the `copy_to()` function. The `copy_to()` function receives two additional arguments, once the new job name for the copy of the job object `new_job_name` and in addition we do not want the job to be saved in the databased at this point, so we add the option `new_database_entry=False`. After the strain is applied on the structure using the `apply_strain()` function the updated structure is set to the structure property of the copied job and the job is executed by calling the `run()` function of the job object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "33593b44-c030-4b24-978c-49d595752eda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job lmp_0_95 was saved and received the ID: 21752\n",
      "The job lmp_0_96 was saved and received the ID: 21753\n",
      "The job lmp_0_97 was saved and received the ID: 21754\n",
      "The job lmp_0_98 was saved and received the ID: 21755\n",
      "The job lmp_0_99 was saved and received the ID: 21756\n",
      "The job lmp_1_0 was saved and received the ID: 21757\n",
      "The job lmp_1_01 was saved and received the ID: 21758\n",
      "The job lmp_1_02 was saved and received the ID: 21759\n",
      "The job lmp_1_03 was saved and received the ID: 21760\n",
      "The job lmp_1_04 was saved and received the ID: 21761\n",
      "The job lmp_1_05 was saved and received the ID: 21762\n"
     ]
    }
   ],
   "source": [
    "for strain in strain_lst: \n",
    "    job_strain = job_template.copy_to(\n",
    "        new_job_name=\"lmp_\" + str(1+strain).replace(\".\", \"_\"), \n",
    "        new_database_entry=False\n",
    "    )\n",
    "    structure_copy = structure_template.copy()\n",
    "    structure_copy.apply_strain(strain)\n",
    "    job_strain.structure = structure_copy \n",
    "    job_strain.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84958be8-4799-4608-b1e2-7fd96ce1f3e6",
   "metadata": {},
   "source": [
    "All calcultion are summarized in the job table of the project object. In addition to the database ID this table contains the location of the job object given by the `projectpath` and `project` as well as the execution time and further details about the calculation. Still it is important to mention that the `job_table()` and the database in pyiron in general is just an index of all calculation executed by pyiron. The results of the calculation are stored in the HDF5 files of the individual calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80d5c168-92f5-4ed7-8a6a-0153e9df4a95",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>status</th>\n",
       "      <th>chemicalformula</th>\n",
       "      <th>job</th>\n",
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       "      <td>None</td>\n",
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       "      <td>21753</td>\n",
       "      <td>finished</td>\n",
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       "      <td>/lmp_0_96</td>\n",
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       "      <td>pyiron@ljubi#1</td>\n",
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       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21754</td>\n",
       "      <td>finished</td>\n",
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       "      <td>lmp_0_97</td>\n",
       "      <td>/lmp_0_97</td>\n",
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       "      <td>2023-08-11-lammps-workshop/evcurve/</td>\n",
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       "      <td>2023-08-11 09:37:00.962842</td>\n",
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       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21755</td>\n",
       "      <td>finished</td>\n",
       "      <td>Al4</td>\n",
       "      <td>lmp_0_98</td>\n",
       "      <td>/lmp_0_98</td>\n",
       "      <td>/home/janssen/pyiron/projects/</td>\n",
       "      <td>2023-08-11-lammps-workshop/evcurve/</td>\n",
       "      <td>2023-08-11 09:37:01.334911</td>\n",
       "      <td>2023-08-11 09:37:01.816432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pyiron@ljubi#1</td>\n",
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       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21756</td>\n",
       "      <td>finished</td>\n",
       "      <td>Al4</td>\n",
       "      <td>lmp_0_99</td>\n",
       "      <td>/lmp_0_99</td>\n",
       "      <td>/home/janssen/pyiron/projects/</td>\n",
       "      <td>2023-08-11-lammps-workshop/evcurve/</td>\n",
       "      <td>2023-08-11 09:37:02.136382</td>\n",
       "      <td>2023-08-11 09:37:02.686070</td>\n",
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       "      <td>pyiron@ljubi#1</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21757</td>\n",
       "      <td>finished</td>\n",
       "      <td>Al4</td>\n",
       "      <td>lmp_1_0</td>\n",
       "      <td>/lmp_1_0</td>\n",
       "      <td>/home/janssen/pyiron/projects/</td>\n",
       "      <td>2023-08-11-lammps-workshop/evcurve/</td>\n",
       "      <td>2023-08-11 09:37:03.010487</td>\n",
       "      <td>2023-08-11 09:37:03.521878</td>\n",
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       "      <td>pyiron@ljubi#1</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21758</td>\n",
       "      <td>finished</td>\n",
       "      <td>Al4</td>\n",
       "      <td>lmp_1_01</td>\n",
       "      <td>/lmp_1_01</td>\n",
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       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21759</td>\n",
       "      <td>finished</td>\n",
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       "      <td>lmp_1_02</td>\n",
       "      <td>/lmp_1_02</td>\n",
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       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21760</td>\n",
       "      <td>finished</td>\n",
       "      <td>Al4</td>\n",
       "      <td>lmp_1_03</td>\n",
       "      <td>/lmp_1_03</td>\n",
       "      <td>/home/janssen/pyiron/projects/</td>\n",
       "      <td>2023-08-11-lammps-workshop/evcurve/</td>\n",
       "      <td>2023-08-11 09:37:05.500825</td>\n",
       "      <td>2023-08-11 09:37:06.030392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pyiron@ljubi#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21761</td>\n",
       "      <td>finished</td>\n",
       "      <td>Al4</td>\n",
       "      <td>lmp_1_04</td>\n",
       "      <td>/lmp_1_04</td>\n",
       "      <td>/home/janssen/pyiron/projects/</td>\n",
       "      <td>2023-08-11-lammps-workshop/evcurve/</td>\n",
       "      <td>2023-08-11 09:37:06.357974</td>\n",
       "      <td>2023-08-11 09:37:06.925999</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21762</td>\n",
       "      <td>finished</td>\n",
       "      <td>Al4</td>\n",
       "      <td>lmp_1_05</td>\n",
       "      <td>/lmp_1_05</td>\n",
       "      <td>/home/janssen/pyiron/projects/</td>\n",
       "      <td>2023-08-11-lammps-workshop/evcurve/</td>\n",
       "      <td>2023-08-11 09:37:07.257593</td>\n",
       "      <td>2023-08-11 09:37:07.826233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pyiron@ljubi#1</td>\n",
       "      <td>Lammps</td>\n",
       "      <td>0.1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    status chemicalformula       job     subjob  \\\n",
       "0   21752  finished             Al4  lmp_0_95  /lmp_0_95   \n",
       "1   21753  finished             Al4  lmp_0_96  /lmp_0_96   \n",
       "2   21754  finished             Al4  lmp_0_97  /lmp_0_97   \n",
       "3   21755  finished             Al4  lmp_0_98  /lmp_0_98   \n",
       "4   21756  finished             Al4  lmp_0_99  /lmp_0_99   \n",
       "5   21757  finished             Al4   lmp_1_0   /lmp_1_0   \n",
       "6   21758  finished             Al4  lmp_1_01  /lmp_1_01   \n",
       "7   21759  finished             Al4  lmp_1_02  /lmp_1_02   \n",
       "8   21760  finished             Al4  lmp_1_03  /lmp_1_03   \n",
       "9   21761  finished             Al4  lmp_1_04  /lmp_1_04   \n",
       "10  21762  finished             Al4  lmp_1_05  /lmp_1_05   \n",
       "\n",
       "                       projectpath                              project  \\\n",
       "0   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "1   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "2   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "3   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "4   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "5   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "6   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "7   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "8   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "9   /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "10  /home/janssen/pyiron/projects/  2023-08-11-lammps-workshop/evcurve/   \n",
       "\n",
       "                    timestart                   timestop  totalcputime  \\\n",
       "0  2023-08-11 09:36:58.578650 2023-08-11 09:36:59.299133           0.0   \n",
       "1  2023-08-11 09:36:59.619686 2023-08-11 09:37:00.127617           0.0   \n",
       "2  2023-08-11 09:37:00.457299 2023-08-11 09:37:00.962842           0.0   \n",
       "3  2023-08-11 09:37:01.334911 2023-08-11 09:37:01.816432           0.0   \n",
       "4  2023-08-11 09:37:02.136382 2023-08-11 09:37:02.686070           0.0   \n",
       "5  2023-08-11 09:37:03.010487 2023-08-11 09:37:03.521878           0.0   \n",
       "6  2023-08-11 09:37:03.844633 2023-08-11 09:37:04.355407           0.0   \n",
       "7  2023-08-11 09:37:04.677583 2023-08-11 09:37:05.176471           0.0   \n",
       "8  2023-08-11 09:37:05.500825 2023-08-11 09:37:06.030392           0.0   \n",
       "9  2023-08-11 09:37:06.357974 2023-08-11 09:37:06.925999           0.0   \n",
       "10 2023-08-11 09:37:07.257593 2023-08-11 09:37:07.826233           0.0   \n",
       "\n",
       "          computer hamilton hamversion parentid masterid  \n",
       "0   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "1   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "2   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "3   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "4   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "5   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "6   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "7   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "8   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "9   pyiron@ljubi#1   Lammps        0.1     None     None  \n",
       "10  pyiron@ljubi#1   Lammps        0.1     None     None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr.job_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3059619-3a38-4bcd-a1b3-2a74e81f1dca",
   "metadata": {},
   "source": [
    "# Collect the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab6e957-53c5-4452-b3ba-09f1f3bc07da",
   "metadata": {},
   "source": [
    "After submitting and successfully executing the individual calculation the next step is collecting the results. While in principle it would be possible to collect the output required to plot the energy volume curve, namely the volume and the total energy at the end of each calculation, the goal of pyiron is to split the execution from the analysis of the calculation. This is particularly beneficial when the calculations can not be completed in the order of seconds but require multiple hours and might be submitted to the queuing system individually. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb7a1b7-c8cb-424c-b950-93bbe2fc2fc0",
   "metadata": {},
   "source": [
    "## Iterate over jobs in project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82247466-fca0-429f-8f1c-8ea28ed8b0a6",
   "metadata": {},
   "source": [
    "To structure the analysis in the pyiron framework we recommend to follow the map-reduce pattern. This means we start by loading one job from the dataset, develop a set of functions we want to apply to all job objects to extract certain quantities and then map these functions to all job objects in a given project. By using the `inspect()` function rather than the `load()` function only a pointer to the HDF5 file of the job object is loaded in contrast to loading the whole job object. This results in a drastic speedup as explained in the next section. Here we use the name of the last job executed in the for loop to load the corresponding job. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21491010-a025-4262-a076-b494371e395e",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_inspect = pr.inspect(job_strain.job_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b3f9d6-8ab5-4499-8073-a2d1fc35f984",
   "metadata": {},
   "source": [
    "Once the pointer to the HDF5 file is loaded we can browse the output and in particular the code-independent `generic` output to find the entries for the volume (`volume`) and the total energy (`energy_tot`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e29542d-8972-4144-8dd6-3cbca3b513c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76.901173453125, -13.0926766182116)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_inspect[\"output/generic/volume\"][0], job_inspect[\"output/generic/energy_tot\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66816ea-4110-4409-aed4-6ec2a8f28a9e",
   "metadata": {},
   "source": [
    "After we identified the properties we want to extract from the individual calculation we define one function per property, which takes a pointer to the HDF5 file of a job object as an input and returns the property it should extract:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1effc61f-360d-476f-bb5a-5a615660d077",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_volume(job):\n",
    "    return job[\"output/generic/volume\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3bc6808d-3d57-45f8-9c38-cf731cdb6659",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_energy_tot(job):\n",
    "    return job[\"output/generic/energy_tot\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae0b254-bae4-4e19-9d9e-33fe6717ebce",
   "metadata": {},
   "source": [
    "In the map step these functions are applied to all jobs in a given project by iterating over the jobs in the project using the `iter_jobs()` function applied on the project object. The results are stored in two lists one for the volumes (`volume_lst`) and one for the total energies (`energy_lst`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "090a7050-fed2-4e9a-a226-b3c44f11263c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad04fce573d843c5bebed891d10ec111",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "volume_lst, energy_lst = [], []\n",
    "for job in pr.iter_jobs():\n",
    "    volume_lst.append(get_volume(job=job))\n",
    "    energy_lst.append(get_energy_tot(job=job))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5f1eea-e615-493c-a3ca-52e27df99590",
   "metadata": {},
   "source": [
    "As a next step we can visualize the aggregated data using the matplotlib library: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f9bc40d-78c1-4788-855f-4ecf8ab0f588",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b21d4b18-f153-4182-9734-5abfcb3e70b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Energy (eV)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(volume_lst, energy_lst)\n",
    "plt.xlabel(\"Volume $\\AA^3$\")\n",
    "plt.ylabel(\"Energy (eV)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08614b54-6d88-4124-ae21-036a770de5a4",
   "metadata": {},
   "source": [
    "## Inspect mode "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100c0530-7415-4f0c-928f-a22091a7e721",
   "metadata": {},
   "source": [
    "While iterating over 11 job objects is sufficiently fast, the `iter_jobs()` function has an optional `convert_to_object` parameter. By setting the `convert_to_object` parameter to `False` the `iter_jobs()` function internally uses the `inspect()` function rather than the `load()` function. This can be several orders of magnitude faster depending on the size of your job objects: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9daee709-44d1-4e7b-8f82-ff1ce2e8d745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6908df316ac49d880529adeb86ece60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "volume_lst, energy_lst = [], []\n",
    "for job in pr.iter_jobs(convert_to_object=False):\n",
    "    volume_lst.append(get_volume(job=job))\n",
    "    energy_lst.append(get_energy_tot(job=job))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f81760-aca7-45ce-94e2-86d0c3be71c1",
   "metadata": {},
   "source": [
    "The output is exactly the same, so we can use the same commands as above to plot the same energy volume curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e0c1d0c7-d2be-4a23-9527-03ad28368412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Energy (eV)')"
      ]
     },
     "execution_count": 19,
     "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": [
    "plt.plot(volume_lst, energy_lst)\n",
    "plt.xlabel(\"Volume $\\AA^3$\")\n",
    "plt.ylabel(\"Energy (eV)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a68bf44f-0e41-4486-a8d8-f402b5be41a7",
   "metadata": {},
   "source": [
    "This ability to drastically accelerate the execution of your simulation workflows by setting specific parameters is a core feature for pyiron. By default we try to provide the users with as much flexibility as possible but at the same time we provide expert users with optional settings to drastically accelerate their simulation workflows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f55c72-6195-4767-bfe3-97e579941e13",
   "metadata": {},
   "source": [
    "## pyiron table "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2242c8ac-9ada-4517-8eba-8a0de2fc23cc",
   "metadata": {},
   "source": [
    "To further simplify the analysis step, pyiron also provides the pyiron tables class. The advantage of this separate class is the ability to submit the analysis to the queuing system and receive the aggregated results as a `pandas.DataFrame` once the map-reduce step is completed. Again, for 11 calculation this might seem like an overhead, but for projects containing thousands of calculation this functionality is essential. The table object is created just like a job object directly from the project. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ead28d53-8a8c-4453-8658-fb3f9ca5f6b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = pr.create.table()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ecdf285-7024-473f-b171-bc2cefcf0ca3",
   "metadata": {},
   "source": [
    "Afterwards the functions defined above, which take a pointer to the HDF5 file of a job object as an input and return a single property are added to the `add` property using the dictionary notation. The keys defined for the individual functions are going to be columns of the resulting `pandas.DataFrame` and the individual calculation in the project correspond to the rows of the `pandas.DataFrame`. Once all functions are defined the table object can be executed using the `run()` function just like a job object and after the execution finished the `pandas.DataFrame` containing all the results is available via the `get_dataframe()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7f640ad-071d-4a06-a576-7c616be41316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job table was saved and received the ID: 21763\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04f2f3716c8e4aed8e49db0317f51e6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading and filtering jobs:   0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e5a2deaf02d4cd99d7a06f9b4253aad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing jobs:   0%|          | 0/11 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.add[\"volume\"] = get_volume\n",
    "table.add[\"energy_tot\"] = get_energy_tot\n",
    "table.run()\n",
    "df = table.get_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c9fb2bc-199f-44dc-9031-c8c20705c43b",
   "metadata": {},
   "source": [
    "The `pandas.DataFrame` contains the `volume` and the `energy_tot` as columns in addition to the `job_id` which is always included and each row represents one of the 11 calculation in the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "63251f15-71dc-4262-a013-59776c7f91a9",
   "metadata": {},
   "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>job_id</th>\n",
       "      <th>volume</th>\n",
       "      <th>energy_tot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21752</td>\n",
       "      <td>56.955528</td>\n",
       "      <td>-13.060681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21753</td>\n",
       "      <td>58.773123</td>\n",
       "      <td>-13.204749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21754</td>\n",
       "      <td>60.628981</td>\n",
       "      <td>-13.309295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21755</td>\n",
       "      <td>62.523502</td>\n",
       "      <td>-13.381820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21756</td>\n",
       "      <td>64.457084</td>\n",
       "      <td>-13.425410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21757</td>\n",
       "      <td>66.430125</td>\n",
       "      <td>-13.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21758</td>\n",
       "      <td>68.443024</td>\n",
       "      <td>-13.425492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21759</td>\n",
       "      <td>70.496180</td>\n",
       "      <td>-13.382455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21760</td>\n",
       "      <td>72.589991</td>\n",
       "      <td>-13.311268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21761</td>\n",
       "      <td>74.724856</td>\n",
       "      <td>-13.213377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21762</td>\n",
       "      <td>76.901173</td>\n",
       "      <td>-13.092677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    job_id     volume  energy_tot\n",
       "0    21752  56.955528  -13.060681\n",
       "1    21753  58.773123  -13.204749\n",
       "2    21754  60.628981  -13.309295\n",
       "3    21755  62.523502  -13.381820\n",
       "4    21756  64.457084  -13.425410\n",
       "5    21757  66.430125  -13.440000\n",
       "6    21758  68.443024  -13.425492\n",
       "7    21759  70.496180  -13.382455\n",
       "8    21760  72.589991  -13.311268\n",
       "9    21761  74.724856  -13.213377\n",
       "10   21762  76.901173  -13.092677"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f403f6-2748-4f5d-b072-3d8c87d56e22",
   "metadata": {},
   "source": [
    "In analogy to the plot above, the `pandas.DataFrame` can also be used to visualize the results using matplotlib. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "50a98015-b804-44d1-8d26-c8b876fa9892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Energy (eV)')"
      ]
     },
     "execution_count": 23,
     "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": [
    "plt.plot(df[\"volume\"], df[\"energy_tot\"])\n",
    "plt.xlabel(\"Volume $\\AA^3$\")\n",
    "plt.ylabel(\"Energy (eV)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ba5984-1d57-4133-90c7-390fe1c92a5a",
   "metadata": {},
   "source": [
    "Again using a `pandas.DataFrame` might look overly complicated for just plotting an energy volume curve, but the intention of this functionality is to analyse hundreds or thousands of calculation. In addition the `pandas.DataFrame` format is supported by a wide range of machine learning packages, so the output of the pyiron table can be directly integrated in an machine learning workflow. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14601d88-834d-4ed6-8fd4-fb6be35d1171",
   "metadata": {},
   "source": [
    "# Murnaghan class "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0180d31-dcf2-4442-8c78-c2a5239e81f2",
   "metadata": {},
   "source": [
    "Still calculating energy volume curves is such a standard task in particular in the ab-initio community, that pyiron provides another abstraction layer. The `Murnaghan` job object named after the Murnaghan, named after the [Murnaghan equation of state](https://en.wikipedia.org/wiki/Murnaghan_equation_of_state) is a `GenericMaster` job object, which basically means it is a job object creating other job objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b43ca960-0149-477f-b03e-034da9b6a7aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "murn = pr.create.job.Murnaghan(job_name=\"murn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd77ebc6-18f1-433f-9acb-bfa695d77278",
   "metadata": {},
   "source": [
    "The `GenericMaster` job objects take a reference job as an input `ref_job` and internally copy this reference job using the `copy_to()` function as demonstrated above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "acd32e75-ac92-4702-902c-4ab5fec07a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "murn.ref_job = job_template"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f2678d7-90c8-40e9-86c8-ceabdaacd4e2",
   "metadata": {},
   "source": [
    "Beyond the `ref_job` the `Murnaghan` job object defines a series of further input arguments, like the number of sample points or the volume range which can all be set via the `input` property using the dictionary notation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fe3dd73d-83ae-48bc-ac39-a63a63dae192",
   "metadata": {},
   "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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>axes</td>\n",
       "      <td>[x, y, z]</td>\n",
       "      <td>Axes along which the strain will be applied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>strains</td>\n",
       "      <td>None</td>\n",
       "      <td>List of strains that should be calculated.  If given vol_range and num_points take no effect.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>allow_aborted</td>\n",
       "      <td>0</td>\n",
       "      <td>The number of child jobs that are allowed to abort, before the whole job is considered aborted.</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",
       "4           axes   [x, y, z]   \n",
       "5        strains        None   \n",
       "6  allow_aborted           0   \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  \n",
       "4                                                      Axes along which the strain will be applied  \n",
       "5    List of strains that should be calculated.  If given vol_range and num_points take no effect.  \n",
       "6  The number of child jobs that are allowed to abort, before the whole job is considered aborted.  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "murn.input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c416f3-e5e9-4d23-8885-e634959b7e64",
   "metadata": {},
   "source": [
    "After both the reference job and the input for the `GenericMaster` job object are defined the `GenericMaster` job object can be executed like any other job object by calling the `run()` function. In particular, this also means that the whole `GenericMaster` job object can be submitted to the queuing system to execute multiple individual calculation either in serial if the number of cores `job.server.cores` of the reference job is euqal to the number of cores of the `GenericMaster` job object `murn.server.cores` or in parallel if the number of cores for the `GenericMaster` job object is sufficiently large. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7cbb2a2a-b29c-4034-8c67-ad49f794547f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn was saved and received the ID: 21764\n",
      "The job murn_0_9 was saved and received the ID: 21765\n",
      "The job murn_0_92 was saved and received the ID: 21766\n",
      "The job murn_0_94 was saved and received the ID: 21767\n",
      "The job murn_0_96 was saved and received the ID: 21768\n",
      "The job murn_0_98 was saved and received the ID: 21769\n",
      "The job murn_1_0 was saved and received the ID: 21770\n",
      "The job murn_1_02 was saved and received the ID: 21771\n",
      "The job murn_1_04 was saved and received the ID: 21772\n",
      "The job murn_1_06 was saved and received the ID: 21773\n",
      "The job murn_1_08 was saved and received the ID: 21774\n",
      "The job murn_1_1 was saved and received the ID: 21775\n"
     ]
    }
   ],
   "source": [
    "murn.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "330675bf-9b46-4503-bf27-0eea924b4727",
   "metadata": {},
   "source": [
    "In addition to simplifying the submission and execution of the calculation the `Murnaghan` job object also provides utility functions like the ability to plot the energy volume curve directly using the `plot()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "907dcf87-5c2b-4974-87eb-f98be2fa5413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Murnaghan: error: 1.065833723414086e-08'}, xlabel='Volume ($\\\\AA^3$)', ylabel='energy (eV)'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "murn.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb56a7b-dfec-4798-8778-65146e4a3694",
   "metadata": {},
   "source": [
    "# Loop over interatomic potentials"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9c86dd-bdad-46ef-b5e8-8dcd0138d856",
   "metadata": {},
   "source": [
    "As already indicated in the beginning we can now combine the functionality of the `Murnaghan` job object and the pyiron table object and iterate over a series of interatomic potentials to compare their equilibrium properties. To do so we create a new project named `potentials`. Again in this project we delete all existing calculation to have a fresh start. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d776c7be-9784-4ce7-a33b-f4ee57d3cef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr = Project(path=\"potentials\")\n",
    "pr.remove_jobs(silently=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2045492-d963-4ca7-9bdc-a051743bd025",
   "metadata": {},
   "source": [
    "We restrict ourselves to the first ten interatomic potentials for aluminium from the `list_potentials()` function, to reduce the number of calculation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "edc42ee1-1312-48f8-83b0-944e9e920452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1',\n",
       " '1996--Farkas-D--Nb-Ti-Al--LAMMPS--ipr1',\n",
       " '1997--Liu-X-Y--Al-Mg--LAMMPS--ipr1',\n",
       " '1998--Liu-X-Y--Al-Mg--LAMMPS--ipr1',\n",
       " '1999--Liu-X-Y--Al-Cu--LAMMPS--ipr1',\n",
       " '1999--Mishin-Y--Al--LAMMPS--ipr1',\n",
       " '2000--Landa-A--Al-Pb--LAMMPS--ipr1',\n",
       " '2000--Sturgeon-J-B--Al--LAMMPS--ipr1',\n",
       " '2002--Mishin-Y--Ni-Al--LAMMPS--ipr1',\n",
       " '2003--Lee-B-J--Al--LAMMPS--ipr1']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "potential_lst = job_template.list_potentials()[:10]\n",
    "potential_lst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791c2721-9a43-4220-9e15-eb8dd209cdda",
   "metadata": {},
   "source": [
    "In addition, we introduce another command to accelerate the execution. This command is switching the `run_mode` from `modal` which is the default to `interactive`. In terms of the LAMMPS execution this means, rather than writing input files and parsing output files we now use the LAMMPS Python interface and directly communicate with it using Python variables. This switch of `run_mode` drastically reduces the I/O overhead and consequently accelerates the calculations.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a2679a1f-e506-4e5b-93ad-a21a0f994eb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_template.server.run_mode.interactive = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1305a69-e677-498f-b812-596f164a63e3",
   "metadata": {},
   "source": [
    "Now iterating over the list of interatomic potentials is as easy as iterating over the different strains in the previous example. We notice that rather than creating a single LAMMPS job object for each strain of a given `Murnaghan` job object, the interactive `run_mode` allows us to reuse the LAMMPS job object, resulting in only one LAMMPS job object for each `Murnaghan` job object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d91a5d26-948b-44f0-811b-ade929b2f4cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_1995__Angelo_J_E__Ni_Al_H__LAMMPS__ipr1 was saved and received the ID: 21776\n",
      "The job murn_1995__Angelo_J_E__Ni_Al_H__LAMMPS__ipr1_lmp_0 was saved and received the ID: 21777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_1996__Farkas_D__Nb_Ti_Al__LAMMPS__ipr1 was saved and received the ID: 21778\n",
      "The job murn_1996__Farkas_D__Nb_Ti_Al__LAMMPS__ipr1_lmp_1 was saved and received the ID: 21779\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "2023-08-11 09:37:20,727 - pyiron_log - WARNING - Minimum could not be found!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_1997__Liu_X_Y__Al_Mg__LAMMPS__ipr1 was saved and received the ID: 21780\n",
      "The job murn_1997__Liu_X_Y__Al_Mg__LAMMPS__ipr1_lmp_2 was saved and received the ID: 21781\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_1998__Liu_X_Y__Al_Mg__LAMMPS__ipr1 was saved and received the ID: 21782\n",
      "The job murn_1998__Liu_X_Y__Al_Mg__LAMMPS__ipr1_lmp_3 was saved and received the ID: 21783\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_1999__Liu_X_Y__Al_Cu__LAMMPS__ipr1 was saved and received the ID: 21784\n",
      "The job murn_1999__Liu_X_Y__Al_Cu__LAMMPS__ipr1_lmp_4 was saved and received the ID: 21785\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_1999__Mishin_Y__Al__LAMMPS__ipr1 was saved and received the ID: 21786\n",
      "The job murn_1999__Mishin_Y__Al__LAMMPS__ipr1_lmp_5 was saved and received the ID: 21787\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_2000__Landa_A__Al_Pb__LAMMPS__ipr1 was saved and received the ID: 21788\n",
      "The job murn_2000__Landa_A__Al_Pb__LAMMPS__ipr1_lmp_6 was saved and received the ID: 21789\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_2000__Sturgeon_J_B__Al__LAMMPS__ipr1 was saved and received the ID: 21790\n",
      "The job murn_2000__Sturgeon_J_B__Al__LAMMPS__ipr1_lmp_7 was saved and received the ID: 21791\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_2002__Mishin_Y__Ni_Al__LAMMPS__ipr1 was saved and received the ID: 21792\n",
      "The job murn_2002__Mishin_Y__Ni_Al__LAMMPS__ipr1_lmp_8 was saved and received the ID: 21793\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job murn_2003__Lee_B_J__Al__LAMMPS__ipr1 was saved and received the ID: 21794\n",
      "The job murn_2003__Lee_B_J__Al__LAMMPS__ipr1_lmp_9 was saved and received the ID: 21795\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:502: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n",
      "/home/janssen/mambaforge/lib/python3.10/site-packages/pyiron_atomistics/lammps/interactive.py:131: UserWarning: Warning: setting upper trangular matrix might slow down the calculation\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "for i, potential in enumerate(potential_lst): \n",
    "    murn = pr.create.job.Murnaghan(job_name=\"murn_\" + potential.replace(\"-\", \"_\"))\n",
    "    job_pot = job_template.copy_to(\n",
    "        new_job_name=\"lmp_\" + str(i), \n",
    "        new_database_entry=False\n",
    "    )\n",
    "    job_pot.potential = potential \n",
    "    murn.ref_job = job_pot\n",
    "    murn.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5e19e0-4d6e-4d66-bc1b-8cdf779313c4",
   "metadata": {},
   "source": [
    "Again analogy to analysing the energy volume curve above, we first start by analysing the `output` of a single murnaghan object and then define the functions which are applied to each `Murnaghan` job object afterwards. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "549b5223-63fd-45c7-8a4a-565da504d1bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-13.440482175734004, 81.39064021275028, 66.17077428883863)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "murn[\"output/equilibrium_energy\"], murn[\"output/equilibrium_bulk_modulus\"], murn[\"output/equilibrium_volume\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "20e87928-82c9-424c-ad9b-209aa8dfc100",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_equilibrium_energy(job):\n",
    "    return job[\"output/equilibrium_energy\"]\n",
    "\n",
    "def get_equilibrium_volume(job):\n",
    "    return job[\"output/equilibrium_volume\"]\n",
    "\n",
    "def get_equilibrium_bulk_modulus(job):\n",
    "    return job[\"output/equilibrium_bulk_modulus\"]\n",
    "\n",
    "def get_potential_name(job):\n",
    "    return job.job_name.split(\"murn_\")[-1].replace(\"_\", \"-\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f400afe6-4a1f-483e-bcb1-74028e788759",
   "metadata": {},
   "source": [
    "As the project now contains both LAMMPS job objects as well as the `Murnaghan` job objects we define an additional filter function, which filters the job objects based on the `hamilton` entry in the `database_entry`. The filter function again receives a the pointer to the HDF5 file of a job object and returns either `True` or `False` if the job should be included in the map-reduce step or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5b012f3a-696b-46a4-85d1-445ca3262220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Murnaghan'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "murn.database_entry.hamilton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8ca1235d-e642-427c-b13f-e38bbcfaeb88",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_jobs(job):\n",
    "    return job.database_entry.hamilton == 'Murnaghan'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed13513f-64ed-46cc-b522-35b835718022",
   "metadata": {},
   "source": [
    "Finally, The pyiron table is created in analogy to the previous example, the filter function is assigned to the table object in addition to the analysis functions. After the definition of the pyiron table object is completed it is applied to the project by calling the `run()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cca3ce7e-63bb-418b-96a9-4ba11a6e9dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = pr.create.table(job_name=\"potentials\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3694d908-8a00-4bf3-abcf-08670f38ff05",
   "metadata": {},
   "outputs": [],
   "source": [
    "table.filter_function = filter_jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "12c64984-5505-4349-bf68-e784602ebd74",
   "metadata": {},
   "outputs": [],
   "source": [
    "table.add[\"potential_name\"] = get_potential_name\n",
    "table.add[\"equilibrium_energy\"] = get_equilibrium_energy\n",
    "table.add[\"equilibrium_volume\"] = get_equilibrium_volume\n",
    "table.add[\"equilibrium_bulk_modulus\"] = get_equilibrium_bulk_modulus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e2e5ed6d-ebb7-4b5b-be02-546a154bf4cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job potentials was saved and received the ID: 21796\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c202f84a981040cdaadb087415e9b6b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading and filtering jobs:   0%|          | 0/21 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfcbdc59cf6d4f5ab14f9f8a96c2d8c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing jobs:   0%|          | 0/9 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df8b6eed-e302-42d5-871a-6f715e0ed9c8",
   "metadata": {},
   "source": [
    "The table lists the properties calculated for the individual interatomic potentials in a `pandas.DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e2511cc3-4928-4f82-a2d5-516f84e3dfea",
   "metadata": {},
   "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>job_id</th>\n",
       "      <th>potential_name</th>\n",
       "      <th>equilibrium_energy</th>\n",
       "      <th>equilibrium_volume</th>\n",
       "      <th>equilibrium_bulk_modulus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21776</td>\n",
       "      <td>1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1</td>\n",
       "      <td>-13.440397</td>\n",
       "      <td>66.427847</td>\n",
       "      <td>81.040441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21780</td>\n",
       "      <td>1997--Liu-X-Y--Al-Mg--LAMMPS--ipr1</td>\n",
       "      <td>-13.440589</td>\n",
       "      <td>65.580479</td>\n",
       "      <td>83.498560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21782</td>\n",
       "      <td>1998--Liu-X-Y--Al-Mg--LAMMPS--ipr1</td>\n",
       "      <td>-13.441401</td>\n",
       "      <td>65.619037</td>\n",
       "      <td>90.567634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21784</td>\n",
       "      <td>1999--Liu-X-Y--Al-Cu--LAMMPS--ipr1</td>\n",
       "      <td>-13.440403</td>\n",
       "      <td>65.579624</td>\n",
       "      <td>83.423053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21786</td>\n",
       "      <td>1999--Mishin-Y--Al--LAMMPS--ipr1</td>\n",
       "      <td>-13.439988</td>\n",
       "      <td>66.426610</td>\n",
       "      <td>77.697753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21788</td>\n",
       "      <td>2000--Landa-A--Al-Pb--LAMMPS--ipr1</td>\n",
       "      <td>-13.437999</td>\n",
       "      <td>65.511564</td>\n",
       "      <td>78.213776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21790</td>\n",
       "      <td>2000--Sturgeon-J-B--Al--LAMMPS--ipr1</td>\n",
       "      <td>-13.560872</td>\n",
       "      <td>66.447223</td>\n",
       "      <td>80.266269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21792</td>\n",
       "      <td>2002--Mishin-Y--Ni-Al--LAMMPS--ipr1</td>\n",
       "      <td>-13.447738</td>\n",
       "      <td>66.534453</td>\n",
       "      <td>95.971718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21794</td>\n",
       "      <td>2003--Lee-B-J--Al--LAMMPS--ipr1</td>\n",
       "      <td>-13.440482</td>\n",
       "      <td>66.170774</td>\n",
       "      <td>81.390640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   job_id                           potential_name  equilibrium_energy  \\\n",
       "0   21776  1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1          -13.440397   \n",
       "1   21780       1997--Liu-X-Y--Al-Mg--LAMMPS--ipr1          -13.440589   \n",
       "2   21782       1998--Liu-X-Y--Al-Mg--LAMMPS--ipr1          -13.441401   \n",
       "3   21784       1999--Liu-X-Y--Al-Cu--LAMMPS--ipr1          -13.440403   \n",
       "4   21786         1999--Mishin-Y--Al--LAMMPS--ipr1          -13.439988   \n",
       "5   21788       2000--Landa-A--Al-Pb--LAMMPS--ipr1          -13.437999   \n",
       "6   21790     2000--Sturgeon-J-B--Al--LAMMPS--ipr1          -13.560872   \n",
       "7   21792      2002--Mishin-Y--Ni-Al--LAMMPS--ipr1          -13.447738   \n",
       "8   21794          2003--Lee-B-J--Al--LAMMPS--ipr1          -13.440482   \n",
       "\n",
       "   equilibrium_volume  equilibrium_bulk_modulus  \n",
       "0           66.427847                 81.040441  \n",
       "1           65.580479                 83.498560  \n",
       "2           65.619037                 90.567634  \n",
       "3           65.579624                 83.423053  \n",
       "4           66.426610                 77.697753  \n",
       "5           65.511564                 78.213776  \n",
       "6           66.447223                 80.266269  \n",
       "7           66.534453                 95.971718  \n",
       "8           66.170774                 81.390640  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.get_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e9e684-6c02-4591-9589-b18c0900941d",
   "metadata": {},
   "source": [
    "# Summary "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f4d992-0036-4468-ae20-d06ae936c076",
   "metadata": {},
   "source": [
    "The second tutorial illustrates two things, on the one hand how the pre-defined classes in pyiron allow the user to up-scale their simulation protocols and on the other hand how expert commands like the `inspect()` function or switching the `run_mode` can be leveraged in pyiron to drastically accelerate the execution of a given simulation protocol. This combination makes pyiron suitable for parameter studies for interatomic potentials. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0522455d-1c09-42b1-a0a2-2dfc1bb8c991",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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