32: Tungsten SCDM parameters from projectability¶
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Outline: Compute the Wannier interpolated band structure of tungsten (W) using the SCDM method to calculate the initial guess (see Tutorial 27 for more details). The free parameters in the SCDM method, i.e., \(\mu\) and \(\sigma\), are obtained by fitting a complementary error function to the projectabilities. The number of MLWFs is given by the number of pseudo-atomic orbitals (PAOs) in the pseudopotential, \(21\) in this case. All the steps shown in this tutorial have been automated in the AiiDA1 workflow that can be downloaded from the MaterialsCloud website2.
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Directory:
tutorials/tutorial32/
Files can be downloaded from here -
Input files
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W.scf
Thepwscf
input file for ground state calculation -
W.nscf
Thepwscf
input file to obtain Bloch states on a uniform grid -
W.pw2wan
The input file forpw2wannier90
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W.proj
The input file forprojwfc
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generate_weights.sh
The bash script to extract the projectabilities from the output ofprojwfc
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W.win
Thewannier90
input file
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Run
pwscf
to obtain the ground state of tungsten -
Run
pwscf
to obtain the Bloch states on a \(10\times10\times10\) uniform \(k\)-point grid -
Run
wannier90
to generate a list of the required overlaps (written into theW.nnkp
file) -
Run
projwfc
to compute the projectabilities of the Bloch states onto the Bloch sums obtained from the PAOs in the pseudopotential -
Run
generate_weights
to extract the projectabilitites fromproj.out
in a format suitable to be read byXmgrace
or gnuplot -
Plot the projectabilities and fit the data with the complementary error function
\[ f(\epsilon;\mu,\sigma) = \frac{1}{2} \mathrm{erfc}(-\frac{\mu - \epsilon}{\sigma}). \]We are going to use
Xmgrace
to plot the projectabilities and perform the fitting. OpenXmgrace
To Import the
p_vs_e.dat
file, click onData
from the top bar and thenImport -> ASCII...
. At this point a new windowGrace: Read sets
should pop up. Selectp_vs_e.dat
in theFiles
section, clickOk
at the bottom and close the window. You should now be able to see a quite noisy function that is bounded between 1 and 0. You can modify the appearence of the plot by clicking onPlot
in the top bar and thenSet appearance...
. In theMain
section of the pop-up window change the symbol type fromNone
toCircle
. Change the line type from straight to none, since the lines added by default by Xmgrace are not meaningful. For the fitting, go toData -> Transformations -> Non-linear curve fitting
. In this window, select the source from theSet
box and in theFormula
box insert the followingSelect 2 as number of parameters, give 40 as initial condition for
A0
and 7 forA1
. ClickApply
. A new window should pop up with the stats of the fitting. In particular you should find aCorrelation coefficient
of 0.96 and a value of \(39.9756\) forA0
and \(6.6529\) forA1
. These are the value of \(\mu_{fit}\) and \(\sigma_{fit}\) we are going to use for the SCDM method. In particular, \(\mu_{SCDM} = \mu_{fit} - 3\sigma_{fit} = 20.0169\) eV and \(\sigma_{SCDM} = \sigma_{fit} = 6.6529\) eV. The motivation for this specific choice of \(\mu_{fit}\) and \(\sigma_{fit}\) may be found in Ref. 3, where the authors also show validation of this approach on a dataset of 200 materials. You should now see the fitting function, as well as the projectabilities, in the graph below. -
Open
W.pw2wan
and append the following lines -
Run
pw2wannier90
to compute the overlaps between Bloch states and the projections for the starting guess (written in theW.mmn
andW.amn
files) -
Run
wannier90
to obtain the interpolated bandstructure (see the band structure plot).Please cite Ref. 3 in any publication employing the procedure outlined in this tutorial to obtain \(\mu\) and \(\sigma\).
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Giovanni Pizzi, Andrea Cepellotti, Riccardo Sabatini, Nicola Marzari, and Boris Kozinsky. Aiida: automated interactive infrastructure and database for computational science. Computational Materials Science, 111:218 – 230, 2016. ↩
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V. Vitale, G. Pizzi, A. Marrazzo, J. R. Yates, N. Marzari, and A. A. Mostofi. Automated high-throughput wannierisation. Materials Cloud Archive, 2019. doi:\href http://doi.org/10.24435/materialscloud:2019.0044/v210.24435/materialscloud:2019.0044/v2. ↩
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Valerio Vitale, Giovanni Pizzi, Antimo Marrazzo, Jonathan Yates, Nicola Marzari, and Arash Mostofi. Automated high-throughput wannierisation. 2019. arXiv:1909.00433. ↩↩↩