jobflow_qe_to_universal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8676f0ca-5b34-4867-a32d-928f37fb0ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import import_module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62fe38d4-f022-4cce-afff-52a1b2dcd611",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import subprocess\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from ase.build import bulk\n",
    "from ase.io import write\n",
    "from ase.atoms import Atoms\n",
    "from adis_tools.parsers import parse_pw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1bd1f75c-a07f-48c9-ae56-22ba674d6d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jobflow import job, Flow\n",
    "from jobflow.managers.local import run_locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e73226b-0cd2-4e01-b11a-3755dcb88291",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_function_dict(flow):\n",
    "    return {\n",
    "        job.uuid: job.function\n",
    "        for job in flow.jobs\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59b66e56-f5f4-4ddd-8304-cdeda737e2a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_nodes_dict(function_dict):\n",
    "    nodes_dict, nodes_mapping_dict = {}, {}\n",
    "    # function_dict_len = len(function_dict)\n",
    "    for i, [k, v] in enumerate(function_dict.items()):\n",
    "        # nodes_dict[function_dict_len-i] = v\n",
    "        # nodes_mapping_dict[k] = function_dict_len-i\n",
    "        nodes_dict[i] = v\n",
    "        nodes_mapping_dict[k] = i\n",
    "    \n",
    "    return nodes_dict, nodes_mapping_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c08a835-f088-4eec-a3b2-eb15a7d6a7a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_edge_from_dict(target, key, value_dict, nodes_mapping_dict):\n",
    "    if len(value_dict['attributes']) == 1:\n",
    "        return {'target': target, 'targetHandle': key, \"source\": nodes_mapping_dict[value_dict['uuid']], 'sourceHandle': value_dict['attributes'][0][1]}\n",
    "    else:\n",
    "        return {'target': target, 'targetHandle': key, \"source\": nodes_mapping_dict[value_dict['uuid']], 'sourceHandle': None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "359b693a-e7eb-418a-be3e-640ed0d9ed5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_edges_and_extend_nodes(flow_dict, nodes_mapping_dict, nodes_dict):\n",
    "    edges_lst = []\n",
    "    for job in flow_dict['jobs']:\n",
    "        for k, v in job['function_kwargs'].items():\n",
    "            if isinstance(v, dict) and '@module' in v and '@class' in v and '@version' in v:\n",
    "                edges_lst.append(get_edge_from_dict(\n",
    "                    target=nodes_mapping_dict[job[\"uuid\"]], \n",
    "                    key=k, \n",
    "                    value_dict=v, \n",
    "                    nodes_mapping_dict=nodes_mapping_dict,\n",
    "                ))\n",
    "            elif isinstance(v, dict) and any([isinstance(el, dict) and '@module' in el and '@class' in el and '@version' in el for el in v.values()]):\n",
    "                # print(\"found link in dict\", v)\n",
    "                node_dict_index = len(nodes_dict)\n",
    "                nodes_dict[node_dict_index] = get_dict\n",
    "                for kt, vt in v.items():\n",
    "                    if isinstance(vt, dict) and '@module' in vt and '@class' in vt and '@version' in vt:\n",
    "                        edges_lst.append(get_edge_from_dict(\n",
    "                            target=node_dict_index, \n",
    "                            key=kt, \n",
    "                            value_dict=vt, \n",
    "                            nodes_mapping_dict=nodes_mapping_dict,\n",
    "                        ))\n",
    "                    else:\n",
    "                        if vt not in nodes_dict.values():\n",
    "                            node_index = len(nodes_dict)\n",
    "                            nodes_dict[node_index] = vt\n",
    "                        else:\n",
    "                            # print(nodes_dict, vt)\n",
    "                            node_index = {str(tv): tk for tk, tv in nodes_dict.items()}[str(vt)]\n",
    "                        edges_lst.append({'target': node_dict_index, 'targetHandle': kt, \"source\": node_index, 'sourceHandle': None})\n",
    "                edges_lst.append({'target': nodes_mapping_dict[job[\"uuid\"]], 'targetHandle': k, \"source\": node_dict_index, 'sourceHandle': None})\n",
    "            elif isinstance(v, list) and any([isinstance(el, dict) and '@module' in el and '@class' in el and '@version' in el for el in v]):\n",
    "                # print(\"found link in list\", v)\n",
    "                node_list_index = len(nodes_dict)\n",
    "                nodes_dict[node_list_index] = get_list\n",
    "                for kt, vt in enumerate(v):\n",
    "                    if isinstance(vt, dict) and '@module' in vt and '@class' in vt and '@version' in vt:\n",
    "                        edges_lst.append(get_edge_from_dict(\n",
    "                            target=node_list_index, \n",
    "                            key=str(kt), \n",
    "                            value_dict=vt, \n",
    "                            nodes_mapping_dict=nodes_mapping_dict,\n",
    "                        ))\n",
    "                    else:\n",
    "                        if vt not in nodes_dict.values():\n",
    "                            node_index = len(nodes_dict)\n",
    "                            nodes_dict[node_index] = vt\n",
    "                        else:\n",
    "                            # print(nodes_dict, vt)\n",
    "                            node_index = {str(tv): tk for tk, tv in nodes_dict.items()}[str(vt)]\n",
    "                        edges_lst.append({'target': node_list_index, 'targetHandle': kt, \"source\": node_index, 'sourceHandle': None})\n",
    "                edges_lst.append({'target': nodes_mapping_dict[job[\"uuid\"]], 'targetHandle': k, \"source\": node_list_index, 'sourceHandle': None})\n",
    "            else:\n",
    "                if v not in nodes_dict.values():\n",
    "                    node_index = len(nodes_dict)\n",
    "                    nodes_dict[node_index] = v\n",
    "                else:\n",
    "                    node_index = {tv: tk for tk, tv in nodes_dict.items()}[v]\n",
    "                edges_lst.append({'target': nodes_mapping_dict[job[\"uuid\"]], 'targetHandle': k, \"source\": node_index, 'sourceHandle': None})\n",
    "    return edges_lst, nodes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a349966-1bf5-4851-b065-bd0e9df11bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dict(**kwargs):\n",
    "    return {k: v for k, v in kwargs.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "565093ff-3405-4485-aeb3-6dcc25173e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_list(**kwargs):\n",
    "    return list(kwargs.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9da72034-071d-49e5-b7e0-14f31ac1fba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_input(input_dict, working_directory=\".\"):\n",
    "    filename = os.path.join(working_directory, \"input.pwi\")\n",
    "    os.makedirs(working_directory, exist_ok=True)\n",
    "    write(\n",
    "        filename=filename,\n",
    "        images=Atoms(**input_dict[\"structure\"]),\n",
    "        Crystal=True,\n",
    "        kpts=input_dict[\"kpts\"],\n",
    "        input_data={\n",
    "            \"calculation\": input_dict[\"calculation\"],\n",
    "            \"occupations\": \"smearing\",\n",
    "            \"degauss\": input_dict[\"smearing\"],\n",
    "        },\n",
    "        pseudopotentials=input_dict[\"pseudopotentials\"],\n",
    "        tstress=True,\n",
    "        tprnfor=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ff12d3c-7599-49ce-8cea-559dbe2f4ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_output(working_directory=\".\"):\n",
    "    output = parse_pw(os.path.join(working_directory, \"pwscf.xml\"))\n",
    "    return {\n",
    "        \"structure\": output[\"ase_structure\"].todict(),\n",
    "        \"energy\": output[\"energy\"],\n",
    "        \"volume\": output[\"ase_structure\"].get_volume(),\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "738792db-f97c-49b5-8a7c-ea383c68a003",
   "metadata": {},
   "outputs": [],
   "source": [
    "@job(data=[\"energy\", \"volume\", \"structure\"])\n",
    "def calculate_qe(working_directory, input_dict):\n",
    "    write_input(\n",
    "        input_dict=input_dict,\n",
    "        working_directory=working_directory,\n",
    "    )\n",
    "    subprocess.check_output(\n",
    "        \"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n",
    "        cwd=working_directory,\n",
    "        shell=True,\n",
    "    )\n",
    "    return collect_output(working_directory=working_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "532bf710-0497-4090-8d09-16ec929c2a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "@job(data=[\"i\" for i in range(100)])  # maximum number of strains is currently 100 \n",
    "def generate_structures(structure, strain_lst):\n",
    "    structure_lst = []\n",
    "    for strain in strain_lst:\n",
    "        structure_strain = Atoms(**structure)\n",
    "        structure_strain.set_cell(\n",
    "            structure_strain.cell * strain ** (1 / 3), scale_atoms=True\n",
    "        )\n",
    "        structure_lst.append(structure_strain)\n",
    "    return {str(i): s.todict() for i, s in enumerate(structure_lst)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c7dd46e-9d3f-40a5-b09e-df7ff1f4b552",
   "metadata": {},
   "outputs": [],
   "source": [
    "@job\n",
    "def plot_energy_volume_curve(volume_lst, energy_lst):\n",
    "    plt.plot(volume_lst, energy_lst)\n",
    "    plt.xlabel(\"Volume\")\n",
    "    plt.ylabel(\"Energy\")\n",
    "    plt.savefig(\"evcurve.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fadf2b4f-cc59-47e5-9994-673c6f5bc212",
   "metadata": {},
   "outputs": [],
   "source": [
    "@job\n",
    "def get_bulk_structure(name, a, cubic):\n",
    "    return bulk(\n",
    "        name=name,\n",
    "        a=a,\n",
    "        cubic=cubic,\n",
    "    ).todict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "763d5797-8d0f-4519-82b2-bb7ba0c8ff98",
   "metadata": {},
   "outputs": [],
   "source": [
    "pseudopotentials = {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8560edf8-ed38-49e9-b5f8-37a7ddb2d7fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "structure = get_bulk_structure(\n",
    "    name=\"Al\",\n",
    "    a=4.05,\n",
    "    cubic=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "22c3b8dd-83cc-416d-832f-d8abb1bd1478",
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_mini = calculate_qe(\n",
    "    working_directory=\"mini\",\n",
    "    input_dict={\n",
    "        \"structure\": structure.output,\n",
    "        \"pseudopotentials\": pseudopotentials,\n",
    "        \"kpts\": (3, 3, 3),\n",
    "        \"calculation\": \"vc-relax\",\n",
    "        \"smearing\": 0.02,\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff0addeb-5ded-4f57-8ca4-ae79f9e96369",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_strains = 5\n",
    "structure_lst = generate_structures(\n",
    "    structure=calc_mini.output.structure,\n",
    "    strain_lst=np.linspace(0.9, 1.1, number_of_strains),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8aff01a2-fddf-4506-8527-0a6662a2836e",
   "metadata": {},
   "outputs": [],
   "source": [
    "job_strain_lst = []\n",
    "for i in range(number_of_strains):\n",
    "    calc_strain = calculate_qe(\n",
    "        working_directory=\"strain_\" + str(i),\n",
    "        input_dict={\n",
    "            \"structure\": getattr(structure_lst.output, str(i)),\n",
    "            \"pseudopotentials\": pseudopotentials,\n",
    "            \"kpts\": (3, 3, 3),\n",
    "            \"calculation\": \"scf\",\n",
    "            \"smearing\": 0.02,\n",
    "        },\n",
    "    )\n",
    "    job_strain_lst.append(calc_strain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bcd97640-9713-4816-8395-85a081275eb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = plot_energy_volume_curve(\n",
    "    volume_lst=[job.output.volume for job in job_strain_lst],\n",
    "    energy_lst=[job.output.energy for job in job_strain_lst],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f6fdd39-df5d-4cf8-a987-093a83c4954f",
   "metadata": {},
   "outputs": [],
   "source": [
    "flow = Flow([structure, calc_mini, structure_lst] + job_strain_lst + [plot])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95e87e64-f3b1-4279-bda4-4d05fa110326",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7c0a68f-d8c3-4bae-8b4a-c0742ca4c4e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce14fd7b-58fa-4343-86d2-7e701cdbd927",
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_dict = flow.as_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b605fea-4bd1-42e3-b248-97ae3b1a50ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72f40946-542a-412a-8c70-f4f185a49319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'e556acdb-0504-4ee8-9230-556b6204d556': <function __main__.get_bulk_structure(name, a, cubic)>,\n",
       " 'e9b6361d-0b18-4c6c-88f3-e247a0659e34': <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 'db93730a-db3f-4d00-8ec9-2f7111f4245e': <function __main__.generate_structures(structure, strain_lst)>,\n",
       " '1ad51625-8409-4820-bf12-6ffda8cb9e8a': <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " '5d22fd8f-62b6-4aa6-bc57-baacd91750cb': <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " '3a3b9a2a-5f77-4182-a402-0452c7d99269': <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 'cb06da66-213b-4df3-81a7-6ba7a117efda': <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 'c2b91326-cc79-4251-b468-b6b14b45ed07': <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " '385493c9-e2fe-4237-9b33-ca3bb3481e75': <function __main__.plot_energy_volume_curve(volume_lst, energy_lst)>}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function_dict = get_function_dict(flow=flow)\n",
    "function_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c96dc865-e7b9-476c-a727-3e1329a232a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: <function __main__.get_bulk_structure(name, a, cubic)>,\n",
       " 1: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 2: <function __main__.generate_structures(structure, strain_lst)>,\n",
       " 3: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 4: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 5: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 6: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 7: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 8: <function __main__.plot_energy_volume_curve(volume_lst, energy_lst)>}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes_dict, nodes_mapping_dict = get_nodes_dict(function_dict=function_dict)\n",
    "nodes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fed5ad30-e268-47b5-875f-fcfbbe34724f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'e556acdb-0504-4ee8-9230-556b6204d556': 0,\n",
       " 'e9b6361d-0b18-4c6c-88f3-e247a0659e34': 1,\n",
       " 'db93730a-db3f-4d00-8ec9-2f7111f4245e': 2,\n",
       " '1ad51625-8409-4820-bf12-6ffda8cb9e8a': 3,\n",
       " '5d22fd8f-62b6-4aa6-bc57-baacd91750cb': 4,\n",
       " '3a3b9a2a-5f77-4182-a402-0452c7d99269': 5,\n",
       " 'cb06da66-213b-4df3-81a7-6ba7a117efda': 6,\n",
       " 'c2b91326-cc79-4251-b468-b6b14b45ed07': 7,\n",
       " '385493c9-e2fe-4237-9b33-ca3bb3481e75': 8}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes_mapping_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "832fbf73-a980-4eac-95be-32c389927218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: <function __main__.get_bulk_structure(name, a, cubic)>,\n",
       " 1: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 2: <function __main__.generate_structures(structure, strain_lst)>,\n",
       " 3: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 4: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 5: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 6: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 7: <function __main__.calculate_qe(working_directory, input_dict)>,\n",
       " 8: <function __main__.plot_energy_volume_curve(volume_lst, energy_lst)>,\n",
       " 9: 'Al',\n",
       " 10: 4.05,\n",
       " 11: True,\n",
       " 12: 'mini',\n",
       " 13: <function __main__.get_dict(**kwargs)>,\n",
       " 14: {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'},\n",
       " 15: [3, 3, 3],\n",
       " 16: 'vc-relax',\n",
       " 17: 0.02,\n",
       " 18: [0.9, 0.9500000000000001, 1.0, 1.05, 1.1],\n",
       " 19: 'strain_0',\n",
       " 20: <function __main__.get_dict(**kwargs)>,\n",
       " 21: 'scf',\n",
       " 22: 'strain_1',\n",
       " 23: <function __main__.get_dict(**kwargs)>,\n",
       " 24: 'strain_2',\n",
       " 25: <function __main__.get_dict(**kwargs)>,\n",
       " 26: 'strain_3',\n",
       " 27: <function __main__.get_dict(**kwargs)>,\n",
       " 28: 'strain_4',\n",
       " 29: <function __main__.get_dict(**kwargs)>,\n",
       " 30: <function __main__.get_list(**kwargs)>,\n",
       " 31: <function __main__.get_list(**kwargs)>}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_lst, nodes_dict = get_edges_and_extend_nodes(flow_dict=flow_dict, nodes_mapping_dict=nodes_mapping_dict, nodes_dict=nodes_dict)\n",
    "nodes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6caded1d-09ce-4b4d-a79f-871b4ebe1e47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'target': 0, 'targetHandle': 'name', 'source': 9, 'sourceHandle': None},\n",
       " {'target': 0, 'targetHandle': 'a', 'source': 10, 'sourceHandle': None},\n",
       " {'target': 0, 'targetHandle': 'cubic', 'source': 11, 'sourceHandle': None},\n",
       " {'target': 1,\n",
       "  'targetHandle': 'working_directory',\n",
       "  'source': 12,\n",
       "  'sourceHandle': None},\n",
       " {'target': 13,\n",
       "  'targetHandle': 'structure',\n",
       "  'source': 0,\n",
       "  'sourceHandle': None},\n",
       " {'target': 13,\n",
       "  'targetHandle': 'pseudopotentials',\n",
       "  'source': 14,\n",
       "  'sourceHandle': None},\n",
       " {'target': 13, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
       " {'target': 13,\n",
       "  'targetHandle': 'calculation',\n",
       "  'source': 16,\n",
       "  'sourceHandle': None},\n",
       " {'target': 13,\n",
       "  'targetHandle': 'smearing',\n",
       "  'source': 17,\n",
       "  'sourceHandle': None},\n",
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       " {'target': 2,\n",
       "  'targetHandle': 'structure',\n",
       "  'source': 1,\n",
       "  'sourceHandle': 'structure'},\n",
       " {'target': 2,\n",
       "  'targetHandle': 'strain_lst',\n",
       "  'source': 18,\n",
       "  'sourceHandle': None},\n",
       " {'target': 3,\n",
       "  'targetHandle': 'working_directory',\n",
       "  'source': 19,\n",
       "  'sourceHandle': None},\n",
       " {'target': 20, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '0'},\n",
       " {'target': 20,\n",
       "  'targetHandle': 'pseudopotentials',\n",
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       " {'target': 20, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
       " {'target': 20,\n",
       "  'targetHandle': 'calculation',\n",
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       " {'target': 20,\n",
       "  'targetHandle': 'smearing',\n",
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       "  'sourceHandle': None},\n",
       " {'target': 3,\n",
       "  'targetHandle': 'input_dict',\n",
       "  'source': 20,\n",
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       " {'target': 4,\n",
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       "  'source': 22,\n",
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       " {'target': 23, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '1'},\n",
       " {'target': 23,\n",
       "  'targetHandle': 'pseudopotentials',\n",
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       " {'target': 23, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
       " {'target': 23,\n",
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       " {'target': 23,\n",
       "  'targetHandle': 'smearing',\n",
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       "  'targetHandle': 'working_directory',\n",
       "  'source': 24,\n",
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       " {'target': 25, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '2'},\n",
       " {'target': 25,\n",
       "  'targetHandle': 'pseudopotentials',\n",
       "  'source': 14,\n",
       "  'sourceHandle': None},\n",
       " {'target': 25, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
       " {'target': 25,\n",
       "  'targetHandle': 'calculation',\n",
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       " {'target': 25,\n",
       "  'targetHandle': 'smearing',\n",
       "  'source': 17,\n",
       "  'sourceHandle': None},\n",
       " {'target': 5,\n",
       "  'targetHandle': 'input_dict',\n",
       "  'source': 25,\n",
       "  'sourceHandle': None},\n",
       " {'target': 6,\n",
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       "  'source': 26,\n",
       "  'sourceHandle': None},\n",
       " {'target': 27, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '3'},\n",
       " {'target': 27,\n",
       "  'targetHandle': 'pseudopotentials',\n",
       "  'source': 14,\n",
       "  'sourceHandle': None},\n",
       " {'target': 27, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
       " {'target': 27,\n",
       "  'targetHandle': 'calculation',\n",
       "  'source': 21,\n",
       "  'sourceHandle': None},\n",
       " {'target': 27,\n",
       "  'targetHandle': 'smearing',\n",
       "  'source': 17,\n",
       "  'sourceHandle': None},\n",
       " {'target': 6,\n",
       "  'targetHandle': 'input_dict',\n",
       "  'source': 27,\n",
       "  'sourceHandle': None},\n",
       " {'target': 7,\n",
       "  'targetHandle': 'working_directory',\n",
       "  'source': 28,\n",
       "  'sourceHandle': None},\n",
       " {'target': 29, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '4'},\n",
       " {'target': 29,\n",
       "  'targetHandle': 'pseudopotentials',\n",
       "  'source': 14,\n",
       "  'sourceHandle': None},\n",
       " {'target': 29, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
       " {'target': 29,\n",
       "  'targetHandle': 'calculation',\n",
       "  'source': 21,\n",
       "  'sourceHandle': None},\n",
       " {'target': 29,\n",
       "  'targetHandle': 'smearing',\n",
       "  'source': 17,\n",
       "  'sourceHandle': None},\n",
       " {'target': 7,\n",
       "  'targetHandle': 'input_dict',\n",
       "  'source': 29,\n",
       "  'sourceHandle': None},\n",
       " {'target': 30, 'targetHandle': '0', 'source': 3, 'sourceHandle': 'volume'},\n",
       " {'target': 30, 'targetHandle': '1', 'source': 4, 'sourceHandle': 'volume'},\n",
       " {'target': 30, 'targetHandle': '2', 'source': 5, 'sourceHandle': 'volume'},\n",
       " {'target': 30, 'targetHandle': '3', 'source': 6, 'sourceHandle': 'volume'},\n",
       " {'target': 30, 'targetHandle': '4', 'source': 7, 'sourceHandle': 'volume'},\n",
       " {'target': 8,\n",
       "  'targetHandle': 'volume_lst',\n",
       "  'source': 30,\n",
       "  'sourceHandle': None},\n",
       " {'target': 31, 'targetHandle': '0', 'source': 3, 'sourceHandle': 'energy'},\n",
       " {'target': 31, 'targetHandle': '1', 'source': 4, 'sourceHandle': 'energy'},\n",
       " {'target': 31, 'targetHandle': '2', 'source': 5, 'sourceHandle': 'energy'},\n",
       " {'target': 31, 'targetHandle': '3', 'source': 6, 'sourceHandle': 'energy'},\n",
       " {'target': 31, 'targetHandle': '4', 'source': 7, 'sourceHandle': 'energy'},\n",
       " {'target': 8,\n",
       "  'targetHandle': 'energy_lst',\n",
       "  'source': 31,\n",
       "  'sourceHandle': None}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f72f2e27-3706-48c9-aec9-4b5dbeee738d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 12:45:33,746 INFO Started executing jobs locally\n",
      "2025-01-17 12:45:34,630 INFO Starting job - get_bulk_structure (e556acdb-0504-4ee8-9230-556b6204d556)\n",
      "2025-01-17 12:45:34,635 INFO Finished job - get_bulk_structure (e556acdb-0504-4ee8-9230-556b6204d556)\n",
      "2025-01-17 12:45:34,636 INFO Starting job - calculate_qe (e9b6361d-0b18-4c6c-88f3-e247a0659e34)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 12:45:47,865 INFO Finished job - calculate_qe (e9b6361d-0b18-4c6c-88f3-e247a0659e34)\n",
      "2025-01-17 12:45:47,866 INFO Starting job - generate_structures (db93730a-db3f-4d00-8ec9-2f7111f4245e)\n",
      "2025-01-17 12:45:47,873 INFO Finished job - generate_structures (db93730a-db3f-4d00-8ec9-2f7111f4245e)\n",
      "2025-01-17 12:45:47,873 INFO Starting job - calculate_qe (1ad51625-8409-4820-bf12-6ffda8cb9e8a)\n",
      "2025-01-17 12:45:50,873 INFO Finished job - calculate_qe (1ad51625-8409-4820-bf12-6ffda8cb9e8a)\n",
      "2025-01-17 12:45:50,874 INFO Starting job - calculate_qe (5d22fd8f-62b6-4aa6-bc57-baacd91750cb)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n",
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 12:45:54,119 INFO Finished job - calculate_qe (5d22fd8f-62b6-4aa6-bc57-baacd91750cb)\n",
      "2025-01-17 12:45:54,120 INFO Starting job - calculate_qe (3a3b9a2a-5f77-4182-a402-0452c7d99269)\n",
      "2025-01-17 12:45:57,541 INFO Finished job - calculate_qe (3a3b9a2a-5f77-4182-a402-0452c7d99269)\n",
      "2025-01-17 12:45:57,542 INFO Starting job - calculate_qe (cb06da66-213b-4df3-81a7-6ba7a117efda)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 12:46:01,329 INFO Finished job - calculate_qe (cb06da66-213b-4df3-81a7-6ba7a117efda)\n",
      "2025-01-17 12:46:01,330 INFO Starting job - calculate_qe (c2b91326-cc79-4251-b468-b6b14b45ed07)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 12:46:05,185 INFO Finished job - calculate_qe (c2b91326-cc79-4251-b468-b6b14b45ed07)\n",
      "2025-01-17 12:46:05,185 INFO Starting job - plot_energy_volume_curve (385493c9-e2fe-4237-9b33-ca3bb3481e75)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 12:46:05,277 INFO Finished job - plot_energy_volume_curve (385493c9-e2fe-4237-9b33-ca3bb3481e75)\n",
      "2025-01-17 12:46:05,278 INFO Finished executing jobs locally\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'e556acdb-0504-4ee8-9230-556b6204d556': {1: Response(output={'numbers': array([13, 13, 13, 13]), 'positions': array([[0.   , 0.   , 0.   ],\n",
       "         [0.   , 2.025, 2.025],\n",
       "         [2.025, 0.   , 2.025],\n",
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       " 'e9b6361d-0b18-4c6c-88f3-e247a0659e34': {1: Response(output={'structure': {'numbers': array([13, 13, 13, 13]), 'positions': array([[0.        , 0.        , 0.        ],\n",
       "         [0.        , 2.02281861, 2.02281861],\n",
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       " '1ad51625-8409-4820-bf12-6ffda8cb9e8a': {1: Response(output={'structure': {'numbers': array([13, 13, 13, 13]), 'positions': array([[0.        , 0.        , 0.        ],\n",
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       "         [0.        , 1.98852691, 1.98852691],\n",
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       "         [0.        , 2.05598545, 2.05598545],\n",
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       "         [0.        , 2.08811542, 2.08811542],\n",
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       "         [0.        , 0.        , 4.17623083]]), 'pbc': array([ True,  True,  True])}, 'energy': -1074.8737904693435, 'volume': 72.83724097549829}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/janssen/notebooks/2025/2025-01-16-jobflow-qe'))},\n",
       " '385493c9-e2fe-4237-9b33-ca3bb3481e75': {1: Response(output=None, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/janssen/notebooks/2025/2025-01-16-jobflow-qe'))}}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = run_locally(flow)\n",
    "result"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}