.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "flow_gallery/run_effmass_dfpt.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_flow_gallery_run_effmass_dfpt.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_flow_gallery_run_effmass_dfpt.py:


Effective masses with DFPT
==========================

Flow to compute effective masses with DFPT.
Two options are available:

    - EffMassDFPTWork --> Run DFPT calculation directly assuming the location
                          of the band edges is already known.
    - EffMassAutoDFPTWork --> Run NSCF calculation to find band edges, then use DFPT.

.. GENERATED FROM PYTHON SOURCE LINES 13-118

.. code-block:: default


    import sys
    import os
    import abipy.data as abidata
    import abipy.abilab as abilab
    import abipy.flowtk as flowtk


    def make_scf_input(usepaw=0, nspinor=1):
        """Returns input for GS-SCF calculation."""
        if nspinor == 1:
            pseudos = abidata.pseudos("14si.pspnc") if usepaw == 0 else abidata.pseudos("Si.GGA_PBE-JTH-paw.xml")
        else:
            pseudos = abidata.pseudos("Si_r.psp8") if usepaw == 0 else abidata.pseudos("Si.GGA_PBE-JTH-paw.xml")

        # See https://docs.abinit.org/tests/v7/Input/t82.in
        structure = dict(
             ntypat=1,
             natom=2,
             typat=[1, 1],
             znucl=14,
             #acell=3 * [10.26310667319252],
             acell=3 * [10.2073557], # 5.4015 Ang
             rprim=[[0.0,  0.5,  0.5],
                    [0.5,  0.0,  0.5],
                    [0.5,  0.5,  0.0]],
             xred=[[0.0 , 0.0 , 0.0],
                   [0.25, 0.25, 0.25]],
        )

        scf_input = abilab.AbinitInput(structure=structure, pseudos=pseudos)

        # Global variables
        nband = 8 if nspinor == 1 else 16
        scf_input.set_vars(
            ecut=8,
            nband=nband,
            nspinor=nspinor,
            nstep=100,
            tolvrs=1e-8,
        )

        if scf_input.ispaw:
            scf_input.set_vars(pawecutdg=2 * scf_input["ecut"])

        # Set k-mesh
        scf_input.set_kmesh(ngkpt=[8, 8, 8], shiftk=[0, 0, 0])

        return scf_input


    def build_flow(options):
        # Set working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
        if not options.workdir:
            options.workdir = os.path.basename(sys.argv[0]).replace(".py", "").replace("run_", "flow_")

        # Get the SCF input (without SOC)
        scf_input = make_scf_input(nspinor=1, usepaw=1)

        # Build the flow.
        from abipy.flowtk.effmass_works import EffMassDFPTWork, EffMassAutoDFPTWork
        flow = flowtk.Flow(workdir=options.workdir, manager=options.manager)

        # Compute effective masses for each k in k0_list.
        # effmass_bands_f90 defines the band range for each k in k0_list
        # Here we are interested in the effective masses at the Gamma point for the valence bands
        effmass_bands_f90 = [1, 4] if scf_input["nspinor"] == 1 else [1, 8]
        work = EffMassDFPTWork.from_scf_input(scf_input, k0_list=(0, 0, 0),
                                              effmass_bands_f90=effmass_bands_f90)
        flow.register_work(work)

        # or use this Work to detect band edges automatically but increase ndivsm and decrease tolwfr!
        # you may want to use a negative value of ndivsm (e.g. -20) to use the pymatgen density_line
        # convention. This is useful to avoid problems with high-symmetry
        # k-paths containing very small segments.
        # In this case, indeed,  ndivsm > 0 (Abinit variable) can easily generate thousands of k-points.
        work = EffMassAutoDFPTWork.from_scf_input(scf_input, ndivsm=-5, tolwfr=1e-12)
        flow.register_work(work)

        return flow


    # This block generates the thumbnails in the Abipy gallery.
    # You can safely REMOVE this part if you are using this script for production runs.
    if os.getenv("READTHEDOCS", False):
        __name__ = None
        import tempfile
        options = flowtk.build_flow_main_parser().parse_args(["-w", tempfile.mkdtemp()])
        build_flow(options).graphviz_imshow()


    @flowtk.flow_main
    def main(options):
        """
        This is our main function that will be invoked by the script.
        flow_main is a decorator implementing the command line interface.
        Command line args are stored in `options`.
        """
        return build_flow(options)


    if __name__ == "__main__":
        sys.exit(main())





.. image:: /flow_gallery/images/sphx_glr_run_effmass_dfpt_001.png
    :alt: run effmass dfpt
    :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 119-123

Run the script with:

    run_effmass_dfpt -s



.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.648 seconds)


.. _sphx_glr_download_flow_gallery_run_effmass_dfpt.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/abinit/abipy/gh-pages?filepath=notebooks/flow_gallery/run_effmass_dfpt.ipynb
      :alt: Launch binder
      :width: 150 px


  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: run_effmass_dfpt.py <run_effmass_dfpt.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: run_effmass_dfpt.ipynb <run_effmass_dfpt.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_