{
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    {
      "cell_type": "markdown",
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      "source": [
        "\n# Effective quasi-harmonic potential\n\nThis example shows how to fit the effective quasi-harmonic potential\nas a function of volume and temperature.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
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      "source": [
        "import numpy as np\nfrom ase.build import bulk\nfrom tensorpotential.calculator import grace_fm\n\nfrom directupsampling.calculators.emt import EMT\nfrom directupsampling.effqh.effqh import EffQH\nfrom directupsampling.plot_tools import plot\nfrom directupsampling.sampling import GridSampler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Make the initial ASE Atoms object.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "atoms = bulk(\"Al\", \"fcc\", cubic=True) * 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Make two calculators.\nThe first one is a light-weight calculator to sample MD trajectories.\nThe second one is an accurate calculator to compute forces on atoms.\nThe accurate calculator is typically a DFT calculator.\nIn this example, we intead use a GRACE foundation model.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "atoms.calc = EMT()\ncalc_dft = grace_fm(\"GRACE-1L-OMAT\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define a volume-temperature grid using a GridSampler.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "temperature = 20.0  # in K\ngrid_fqh = [(v, temperature) for v in np.linspace(15.0, 18.0, 7)]\neffqh_sampler = GridSampler(atoms, grid_fqh)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run MD calculations.\nFor a relatively light-weight calculator, we can run directly ``calculate_with``.\nFor a DFT calculator, it may be more convenient to first make input files using\n``write_folders``, run DFT calculations in these folders, and then collect\nthe results using ``read_folders``.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "effqh_sampler.sample_snapshots(30)\nsnapshots_for_effqh = effqh_sampler.calculate_with(calc_dft)\n# effqh_sampler.write_folders(\n#     root_path=\"./calcs\",\n#     joblist_file=\"./jobList\",\n#     calc=std_dft,\n# )\n# Run DFT calculations in the folders.\n# snapshots_for_effqh = GridSampler.read_folders(\"./calcs\").snapshot_container"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Fit the effective harmonic potential as a function of volume and temperature.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "effqh = EffQH(atoms, fit_order=3)\neffqh.fit_snapshots(snapshots_for_effqh, method=\"least-squares\")\neffqh.write_fc_fit(\"effqh_force_constants_fit.hdf5\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Check the quasi-harmonic frequencies.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot(effqh.exact_phonons).show()"
      ]
    }
  ],
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