prerequisites/0_1_structure_and_pseudos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a14fd46b",
   "metadata": {},
   "source": [
    "# Setting up a structure\n",
    "\n",
    "***Prerequisites***: we suppose that you have successfully installed and configured AiiDA. For some help, please have a look at the [AiiDA tutorials](https://aiida-tutorials.readthedocs.io/en/latest/).\n",
    "\n",
    "It is possible, using [ASE](https://wiki.fysik.dtu.dk/ase/) (and also [Pymatgen](https://pymatgen.org/) in a similar way), to read a QE in/out file and load a given crystal structure. \n",
    "To create a structure from scratch, see the dedicated [ASE tutorial](https://wiki.fysik.dtu.dk/ase/gettingstarted/tut01_molecule/molecule.html).\n",
    "\n",
    "We start loading the necessary module from ASE package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "619cf913",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ase.io.espresso import read_espresso_in"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73f031b",
   "metadata": {},
   "source": [
    "The next step is to use the `read_espresso_in` method to load the structure from a `pw.x` input file: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8508d352",
   "metadata": {},
   "outputs": [],
   "source": [
    "Silicon = read_espresso_in('../data/silicon.in')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8063eaf6",
   "metadata": {},
   "source": [
    "Now inspect the created `Atoms` object: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0368574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Atoms(symbols='Si2', pbc=True, cell=[[-2.6988037626030605, 0.0, 2.6988037626030605], [0.0, 2.6988037626030605, 2.6988037626030605], [-2.6988037626030605, 2.6988037626030605, 0.0]], initial_magmoms=...)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Silicon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea79f712",
   "metadata": {},
   "source": [
    "The `Silicon` variable contains all the informations needed to store the structure in the AiiDA database. \n",
    "So, we start by loading our AiiDA profile and modules as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac4a75c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Profile<uuid='3745313b017b418697d37cbf16c4e7ef' name='generic'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from aiida import orm, load_profile\n",
    "load_profile()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542cb7fc",
   "metadata": {},
   "source": [
    "We are now ready to create the `StructureData` instance of our structure, and to store it in the AiiDA database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73856d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<StructureData: uuid: b0024168-aaf8-4c3e-aff7-f66a95bb749b (pk: 371)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure = orm.StructureData(ase=Silicon)\n",
    "structure.store()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8475cb1",
   "metadata": {},
   "source": [
    "The structure *id*, as stored in the database, can be accessed via the `pk` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "309c62c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "371"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure.pk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7eb5165c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "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": [
    "from ase.visualize.plot import plot_atoms\n",
    "\n",
    "plot_atoms(Silicon, radii=0.8, rotation=('0x,0y,0z'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb53d93d",
   "metadata": {},
   "source": [
    "# Store pseudopotentials\n",
    "\n",
    "To run AiiDA simulations for DFT and MBPT, we need to store the desired pseudopotential family in the database."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb6e7a6c",
   "metadata": {},
   "source": [
    "The required pseudos for this tutorial are already installed in the Quantum Mobile.\n",
    "However, if you want to use ad-hoc pseudos in the future, you can run in the command line:\n",
    "\n",
    "    aiida-pseudo install family <ARCHIVE> <LABEL> -P pseudo.upf\n",
    "\n",
    "more details are provided in the [aiida-pseudo documentation](https://aiida-pseudo.readthedocs.io/en/latest/howto.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227871b6",
   "metadata": {},
   "source": [
    "When you use protocols, you have to provide families which have one a protocol. SSSP or PseudoDojo. \n",
    "If you want to use another family, for example sg15, you generate the builder protocols with SSSP or PseudoDojo (I suggest to use the latter), then:\n",
    "\n",
    "```python\n",
    "    family = orm.load_group(\"sg15\")\n",
    "    builder.<sublevels_up_to .pw>.pseudos = family.get_pseudos(structure=structure) \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8228d1a4",
   "metadata": {},
   "source": [
    "# Group creation and usage\n",
    "\n",
    "Groups are powerful objects in AiiDA, they allow to group calculations and to easy query them. To see examples of query and creation/management of groups, see [here](https://aiida.readthedocs.io/projects/aiida-core/en/latest/howto/data.html?highlight=groups)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "efc94e45",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_group = orm.Group('Silicon/bulk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9fed813",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    new_group.store()\n",
    "except:\n",
    "    print(\"The group '{}' already exists!\".format(new_group.label))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18010414",
   "metadata": {},
   "source": [
    "You can add nodes to the group, both from here or from the command line. Here you can load a node and add to the group\n",
    "\n",
    "```python   \n",
    "    node = orm.load_node(pk or uuid)\n",
    "    new_group.add_nodes(node)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1c09b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "node = orm.load_node(371) # structure.pk\n",
    "new_group.add_nodes(node)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d401394e",
   "metadata": {},
   "source": [
    "## How to query the StructureData to reuse it\n",
    "\n",
    "For more details on how to query data, please have a look at [here](https://aiida.readthedocs.io/projects/aiida-core/en/latest/howto/query.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b723f29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aiida.orm import QueryBuilder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "97f442ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QueryBuilder(path=[{'entity_type': 'group.core', 'orm_base': 'group', 'tag': 'group', 'joining_keyword': None, 'joining_value': None, 'edge_tag': None, 'outerjoin': False}, {'entity_type': 'data.core.structure.StructureData.', 'orm_base': 'node', 'tag': 'StructureData_1', 'joining_keyword': 'with_group', 'joining_value': 'group', 'edge_tag': 'group--StructureData_1', 'outerjoin': False}], filters={'group': {'type_string': {'like': '%'}, 'label': 'Silicon/bulk'}, 'StructureData_1': {'node_type': {'like': 'data.core.structure.%'}}, 'group--StructureData_1': {}}, project={'group': [], 'StructureData_1': [], 'group--StructureData_1': []}, order_by=[], limit=None, offset=None, distinct=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Querying the structures that belong to the 'promising_structures' group.\n",
    "qb = QueryBuilder()\n",
    "qb.append(orm.Group, filters={'label': 'Silicon/bulk'}, tag='group')\n",
    "qb.append(orm.StructureData, with_group='group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1173563",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uuid: b0024168-aaf8-4c3e-aff7-f66a95bb749b (pk: 371)\n"
     ]
    }
   ],
   "source": [
    "# Print the query results.\n",
    "for structure in qb.all(flat=True):\n",
    "    print(structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "68604e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "our_structure = qb.all()[0][0]"
   ]
  }
 ],
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