{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Estimate the ZPR of band edges with generalized Frohlich model\n\nFlow to estimate the zero-point renormalization at the band edges\nusing the generalized Frohlich model. The flow uses DFPT to compute\nthe effective masses at the band edges (automatically detected by performing a NSCF run with a k-path),\nand uses an external DDB file providing BECS, eps_inf and phonon frequencies at Gamma.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import sys\nimport os\nimport abipy.data as abidata\nimport abipy.abilab as abilab\nimport abipy.flowtk as flowtk\n\n\ndef build_flow(options):\n\n    # Set working directory (default is the name of the script with '.py' removed and \"run_\" replaced by \"flow_\")\n    if not options.workdir:\n        options.workdir = os.path.basename(sys.argv[0]).replace(\".py\", \"\").replace(\"run_\", \"flow_\")\n\n    # Get structure from DDB file.\n    ddb_path = abidata.ref_file(\"refs/mgo_v8t57/mgo_zpr_t57o_DS3_DDB\")\n    with abilab.abiopen(ddb_path) as ddb:\n        structure = ddb.structure\n\n    # Build SCF input using structure from DDB file.\n    pseudos = abidata.pseudos(\"Ca.psp8\", \"O.psp8\")\n    scf_input = abilab.AbinitInput(structure=structure, pseudos=pseudos)\n\n    # Set other input variables. These quantities are system-depedent.\n    # Here we use parameters similar to https://docs.abinit.org/tests/v8/Input/t57.in\n    scf_input.set_vars(\n        nband=12,\n        nbdbuf=2,\n        diemac=6,\n        ecut=30,                # Underconverged ecut.\n        #ecut=15,\n        nstep=100,\n        tolvrs=1e-16,\n        kptrlatt=[-2,  2,  2,   # In cartesian coordinates, this grid is simple cubic\n                   2, -2,  2,\n                   2,  2, -2],\n    )\n\n    # Build the flow to detect band edges, compute effective masses and finally obtain an estimate for the ZPR\n    # BECS/phonons/eps_inf are taken from ddb_node.\n    from abipy.flowtk.effmass_works import FrohlichZPRFlow\n    flow = FrohlichZPRFlow.from_scf_input(options.workdir, scf_input, ddb_node=ddb_path, ndivsm=2, tolwfr=1e-14,\n                                          manager=options.manager)\n    return flow\n\n\n# This block generates the thumbnails in the Abipy gallery.\n# You can safely REMOVE this part if you are using this script for production runs.\nif os.getenv(\"READTHEDOCS\", False):\n    __name__ = None\n    import tempfile\n    options = flowtk.build_flow_main_parser().parse_args([\"-w\", tempfile.mkdtemp()])\n    build_flow(options).graphviz_imshow()\n\n\n@flowtk.flow_main\ndef main(options):\n    \"\"\"\n    This is our main function that will be invoked by the script.\n    flow_main is a decorator implementing the command line interface.\n    Command line args are stored in `options`.\n    \"\"\"\n    return build_flow(options)\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run the script with:\n\n    run_frohlich_zpr_from_ddb.py -s\n\n\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.0"
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  "nbformat": 4,
  "nbformat_minor": 0
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