# Zero-point renormalization of the band gap and temperature-dependent band gaps¶

This tutorial explains how to compute the electron self-energy due to phonons, obtain the zero-point renormalization (ZPR) of the band gap and temperature-dependent band energies within the harmonic approximation. We start with a very brief overview of the many-body formalism in the context of the electron-phonon (e-ph) interaction. Then we discuss how to evaluate the e-ph self-energy and perform typical convergence studies using the polar semiconductor MgO as an example. Further details concerning the implementation are given in [Gonze2019] and [Romero2020]. Additional examples are provided in this jupyter notebook that explains how to use Abipy to automate the calculations and post-process the results for Diamond.

It is assumed the user has already completed the two tutorials RF1 and RF2, and that he/she is familiar with the calculation of ground state (GS) and response properties in particular phonons, Born effective charges and the high-frequency dielectric tensor. The user should have read the introduction tutorial for the EPH code before running these examples.

This lesson should take about 1.5 hour.

## Formalism¶

The electron-phonon self-energy, $\Sigma^{\text{e-ph}}$, describes the renormalization of charged electronic excitations due to the interaction with phonons. This term should be added to the electron-electron (e-e) self-energy $\Sigma^{\text{e-e}}$ that encodes many-body effects induced by the Coulomb interaction beyond the classical electrostatic Hartree potential. The e-e contribution can be estimated using, for instance, the $GW$ approximation but in this tutorial we are mainly interested in $\Sigma^{\text{e-ph}}$ and its temperature dependence.

In semiconductors and insulators, indeed, most of the temperature dependence of the electronic properties at “low” $T$ originates from the e-ph interaction and the thermal expansion of the unit cell (topic that, however, won’t be discussed in this lesson). Corrections due to $\Sigma^{\text{e-e}}$ are obviously important as it is well known that KS gaps computed with LDA/GGA are systematically underestimated compared to experimental data. Nevertheless, the temperature dependence of $\Sigma^{\text{e-e}}$ is rather small as long as $kT$ is smaller than the fundamental gap (let’s say $3 kT < E_{\text{g}}$).

In state-of-the-art ab-initio perturbative methods, the e-ph coupling is described within DFT by expanding the KS effective potential up to the second order in the atomic displacement, and the vibrational properties are computed with DFPT [Gonze1997], [Baroni2001]. Note that anharmonic effects that may become relevant at “high” $T$ are not included in the present formalism.

The e-ph self-energy consists of two terms: the frequency-dependent Fan-Migdal (FM) self-energy and the static and Hermitian Debye-Waller (DW) part (see e.g. [Giustino2017] and references therein):

\Sigma^\eph(\ww, T) = \Sigma^\FM(\ww, T) + \Sigma^{\DW}(T).

The diagonal matrix elements of the FM self-energy in the KS basis set are given by

$$\begin{split} \Sigma^\FM_{n\kk}(\omega, T) = & \sum_{m,\nu} \int_\BZ \frac{d\qq}{\Omega_\BZ} |\gkq|^2 \\ & \times \left[ \frac{n_\qnu(T) + f_{m\kk+\qq}(\ef,T)} {\omega - \emkq + \wqnu + i \eta} \right.\\ & \left. + \frac{n_\qnu(T) + 1 - f_{m\kk+\qq}(\ef,T)} {\omega - \emkq - \wqnu + i \eta} \right] , \end{split} \label{eq:fan_selfen}$$

where $f_{m\kk+\qq}(\ef,T)$ and $n_\qnu(T)$ are the Fermi-Dirac and Bose-Einstein occupation functions with $T$ the physical temperature and $\ef$ the Fermi level that in turns depends on $T$ and the number of electrons per unit cell. The integration is performed over the $\qq$-points in the Brillouin zone (BZ) of volume $\Omega_\BZ$ and $\eta$ is a positive real infinitesimal.

Important

From a mathematical point of view, one should take the limit $\eta \rightarrow 0^+$. At the level of the implementation, the infinitesimal $\eta$ is replaced by a (small) finite value given by the zcut variable that should be subject to convergence studies. More specifically, one should monitor the convergence of the physical properties of interest for zcut $\rightarrow 0^+$ and number of $\qq$-points $\rightarrow \infty$. Convergence studies should start from values of zcut that are comparable to the typical phonon frequency of the system (usually 0.01 eV or smaller). Note that the default value for zcut is 0.1 eV. This value is reasonable for $GW$ calculations but 0.1 eV is, very likely, too large when computing $\Sigma^{\text{e-ph}}$.

The static DW term involves the second order derivative of the KS potential with respect to the nuclear displacements. State-of-the-art implementations approximate the DW contribution with

$$\label{eq:dw_selfen} \Sigma_{n\kk}^{\DW}(T) = \sum_{\qq\nu m} (2 n_{\qq\nu}(T) + 1) \dfrac{g_{mn\nu}^{2,DW}(\kk, \qq)}{\ee_{n\kk} - \ee_{m\kk}},$$

where $g_{mn\nu}^{2,\DW}(\kk,\qq)$ is an effective matrix element that, within the rigid-ion approximation, can be expressed in terms of the standard first-order $\gkq$ matrix elements by exploiting the invariance of the QP energies under infinitesimal translation [Giustino2017].

At the level of the implementation, the number of bands in the two sums is defined by nband while the $\qq$-mesh for the integration is specified by eph_ngqpt_fine (or ddb_ngqpt if the DFPT potentials are not interpolated). The list of temperatures (in Kelvin) is initialized from tmesh.

For the sake of simplicity, we will omit the $T$-dependence in the next equations. Keep in mind, however, that all the expressions in which $\Sigma$ is involved have an additional dependence on the physical temperature $T$.

Important

The EPH code takes advantage of time-reversal and spatial symmetries to reduce the BZ integration to an appropriate irreducible wedge, $\text{IBZ}_\kk$, defined by the little group of the $\kk$-point i.e. the set of point group operations of the crystal that leave the $\kk$-point invariant within a reciprocal lattice vector $\GG$. Calculations at high-symmetry $\kk$-points such as $\Gamma$ are therefore much faster as there are more symmetries that can be exploited (smaller $\text{IBZ}_k$).

This symmetrization procedure is activated by default and can be disabled by setting symsigma to 0 for testing purposes. Note that when symsigma is set to 1, the code performs a final average of the QP results within each degenerate subspace. As a consequence, accidental degeneracies won’t be removed when symsigma is set to 1.

Note that both the FM and the DW term converge slowly with the $\qq$-sampling. Moreover, accurate computation of the real part require the inclusion of a large number of empty states.

In order to accelerate the convergence with nband, the EPH code can replace the contributions given by the high-energy states above a certain band index $M$ with the solution of a non-self-consistent Sternheimer equation in which only the first $M$ states are required. The methodology, proposed in [Gonze2011], is based on a quasi-static approximation in which the phonon frequencies in the denominator of Eq. (\ref{eq:fan_selfen}) are neglected and the frequency dependence of $\Sigma$ is approximated with the value computed at $\omega = \enk$. This approximation is justified when the bands above $M$ are sufficiently high in energy with respect to the $n\kk$ states that must be corrected. The Sternheimer approach requires the specification of eph_stern and getpot_filepath. The parameter $M$ corresponds to the nband input variable that, obviously, cannot be larger than the number of bands stored in the WFK file (the code will abort if this condition is not fulfilled).

### Quasi-particle corrections due to the e-ph coupling¶

Strictly speaking, the quasi-particle (QP) excitations are defined by the solution(s) in the complex plane of the equation $$z = \ee_\nk + \Sigma_\nk^{\text{e-ph}}(z)$$ provided the non-diagonal components of the e-ph self-energy can be neglected. In practice, the problem is usually simplified by seeking approximated solutions along the real axis following two different approaches:

1. on-the-mass-shell
2. linearized QP equation.

In the on-the-mass-shell approximation, the QP energy is simply given by the real part of the self-energy evaluated at the bare KS eigenvalue:

\ee^\QP_\nk = \ee_\nk + \Re\, \Sigma_\nk^{\text{e-ph}}(\ee_\nk).

In the linearized QP equation, on the contrary, the self-energy is Taylor-expanded around the bare KS eigenvalue and the QP correction is obtained using

\ee^\QP_\nk = \ee_\nk + Z_\nk\,\Re \Sigma^\text{e-ph}_\nk(\ee_\nk)

with the renormalization factor $Z_\nk$ given by

Z_\nk = \left(1 - \Re\left[ \frac{\partial\Sigma^\text{e-ph}_{\nk}}{\partial\ee}\right]\Bigg|_{\ee=\ee_\nk} \right)^{-1}.

Note that $Z_\nk$ is approximately equal to the area under the QP peak in the spectral function:

A_\nk(\ww) = -\dfrac{1}{\pi} | \Im G_\nk(\omega) | = -\dfrac{1}{\pi} \dfrac{\Im \Sigma_\nk(\ww)} {(\ww - \ee_\nk - \Re \Sigma_\nk(\ww)) ^ 2 + \Im \Sigma_\nk(\ww) ^ 2}

Since $A_\nk(\ww)$ integrates to 1, values of $Z_\nk$ in the [0.7, 1] range usually indicate the presence of a well-defined QP excitation that may be accompanied by some background and, possibly, additional satellites. Values of $Z_\nk$ greater than one are clearly unphysical and signal the breakdown of the linearized QP equation. The interpretation of these results requires a careful analysis of $A_\nk(\ww)$ and/or additional convergence tests.

Important

Both approaches are implemented in ABINIT although it should be noted that, according to recent works, the on-the-mass-shell approach provides results that are closer to those obtained with more advanced techniques based on the cumulant expansion [Nery2018].

The ZPR of the excitation energy is defined as the difference between the QP energy evaluated at $T$ = 0 and the bare KS energy. In a similar way, one defines the ZPR of the gap as the difference between the QP band gap at $T$ = 0 and the KS gap.

It is worth to stress that the EPH code can compute QP corrections only for $\nk$ states that are present in the input WFK file (a similar requirement is present in the $GW$ code as well). As a consequence, the $\kk$-mesh (ngkpt, nshiftk, shiftk) for the WFK file should be chosen carefully especially if the band edges are not located at high-symmetry $\kk$-points.

Note that there are different approaches one can use to specify the set of $\nk$ states in $\Sigma_{\nk}$. Each approach has pros and cons. The most direct way consists in specifying the $\kk$-points and the band range using the three variables: nkptgw, kptgw, bdgw For instance, in order to compute the correction for the VBM/CBM at $\Gamma$ in silicon (non-magnetic semiconductor with 8 valence electrons per unit cell), one would use:

nkptgw 1
kptgw  0 0 0  # [3, nkptgw] array
bdgw   4 5    # [2, nkptgw] array giving the initial and the last band index
# for each nkptgw k-point


as the index of the valence band is given by 8 / 2 = 4. Obviously, this input file can only provide the ZPR of the direct gap as Si has an indirect fundamental gap. This the most flexible approach but it requires the specification of three variables and, obviously, one should know the positions of the CBM/VBM. Alternatively, one can use gw_qprange or sigma_erange.

Important

When symsigma is set to 1 (default), the code may decide to enlarge the initial value of bdgw so that all degenerate states for that particular $\kk$-point are included in the calculation.

## Typical workflow for ZPR¶

A typical workflow for ZPR calculations involves the following steps (see the introductory e-ph tutorial):

1. GS calculation to obtain the WFK and the DEN file. The $\kk$-mesh should be dense enough to converge both electronic and vibrational properties. Remember to set prtpot to 1 to produce the file with the KS potential required by the Sternheimer method.

2. DFPT calculations for all the IBZ $\qq$-points corresponding to the ab-initio ddb_ngqpt mesh that will be used to perform the Fourier interpolation of the dynamical matrix and of the DFPT potentials. In the simplest case, one uses a $\qq$-mesh that is equal to the GS $\kk$-mesh (sub-meshes are also fine) and the DFPT calculations can directly start from the WFK produced in step #1. Remember to compute $\bm{\epsilon}^{\infty}$, $\bm{Z}^*$ (polar materials) and the dynamical quadrupoles $\bm{Q}^*$ as these quantities are needed for an accurate interpolation of phonon frequencies and DFPT potentials.

3. NSCF computation of a WFK file on a much denser $\kk$-mesh containing the wavevectors where phonon-induced QP corrections are wanted. The NSCF run uses the DEN file produced in step #1. Remember to compute enough empty states so that it is possible to perform convergence studies w.r.t. nband afterwards.

4. Merge the partial DDB and POT files with mrgddb and mrgdvdb, respectively.

5. Start from the DDB/DVDB files produced in step #4 and the WFK file obtained in step #3 to perform ZPR calculations with eph_task 4.

## Getting started¶

Note

Supposing you made your own install of ABINIT, the input files to run the examples are in the ~abinit/tests/ directory where ~abinit is the absolute path of the abinit top-level directory. If you have NOT made your own install, ask your system administrator where to find the package, especially the executable and test files.

To execute the tutorials, create a working directory (Work*) and copy there the input files and the files file of the lesson. This will be explicitly mentioned in the first lessons, that will tell you more about the files file (see also section 1.1). The files file ending with _x (e.g. tbase1_x.files) must be edited every time you start to use a new input file.

Most of the tutorials do not rely on parallelism (except specific tutorials on parallelism). However you can run most of the tutorial examples in parallel, see the topic on parallelism.

In case you work on your own PC or workstation, to make things easier, we suggest you define some handy environment variables by executing the following lines in the terminal:

export ABI_HOME=Replace_with_the_absolute_path_to_the_abinit_top_level_dir
export PATH=$ABI_HOME/src/98_main/:$PATH
export ABI_TESTS=$ABI_HOME/tests/ export ABI_PSPDIR=$ABI_TESTS/Psps_for_tests/  # Pseudopotentials used in examples.


Examples in this tutorial use these shell variables: copy and paste the code snippets into the terminal (remember to set ABI_HOME first!). The ‘export PATH’ line adds the directory containing the executables to your PATH so that you can invoke the code by simply typing abinit in the terminal instead of providing the absolute path.

In this tutorial, we prefer to focus on the use of the EPH code hence we will be using pre-computed DDB and DFPT POT files to bypass the DFPT part. We also provide a DEN.nc file to initialize the NSCF calculations and a POT file with the GS KS potential required to solve the Sternheimer equation.

If git is installed on your machine, one can easily fetch the entire repository (23 MB) with:

git clone https://github.com/abinit/MgO_eph_zpr.git


Alternatively, use wget:

wget https://github.com/abinit/MgO_eph_zpr/archive/master.zip


or curl:

curl -L https://github.com/abinit/MgO_eph_zpr/archive/master.zip -o master.zip


or simply copy the tarball by clicking the “download button” available in the github web page, unzip the file and rename the directory with:

unzip master.zip
mv MgO_eph_zpr-master MgO_eph_zpr


Warning

The directory with the precomputed files must be located in the same working directory in which you will be executing the tutorial and must be named MgO_eph_zpr.

The AbiPy script used to executed the DFPT part is available here. Note that several parameters have been tuned to reach a reasonable compromise between accuracy and computational cost so do not expect the results obtained at the end of the lesson to be fully converged. More specifically, we use norm-conserving pseudopotentials with a cutoff energy ecut of 30 Ha (too low, it should be ~50 Ha). The DFPT computations is done for the set of irreducible $\qq$-points corresponding to a $\Gamma$-centered 4x4x4 $\qq$ mesh (again, too coarse). $\bm{Z}^*$ and $\bm{\ee}^\infty$ are also computed with the same underconverged settings.

Since AbiPy does not support multiple datasets, each directory corresponds to a single calculation. In particular, all the DFPT tasks (atomic perturbations, DDK, electric field perturbation) can be found inside the w1 directory while w0/t0/outdata contains the GS results. and w0/t1/outdata the GSR file with energie on an high-symmetry $\kk$-path.

## How to extract useful info from the output files¶

Since this is the first tutorial that uses precomputed output files, it is worth explaining how to use the terminal and command line utilities such as ncdump, abitk and AbiPy scripts to extract useful information from the pre-computed files before moving to EPH calculations.

First of all, most of the netcdf files produced by ABINIT store the input file in string format in the input_string netcdf variable. This variable may be useful if you need to know the input variables used to produce that particular output file. To print the value of input_string inside the terminal, use the ncdump utility and the syntax:

ncdump -v input_string MgO_eph_zpr/flow_zpr_mgo/w0/t0/outdata/out_DEN.nc

input_string = "jdtset 1 nband 12 ecut 35.0 ngkpt 4 4 4 nshiftk 1 shiftk 0 0 0 tolvrs 1e-12 nstep 150 iomode 3 prtpot 1 diemac 9.0 nbdbuf 4 paral_kgb 0 natom 2 ntypat 2 typat 2 1 znucl 8 12 xred 0.0000000000 0.0000000000 0.0000000000 0.5000000000 0.5000000000 0.5000000000 acell 1.0 1.0 1.0 rprim 0.0000000000 4.0182361526 4.0182361526 4.0182361526 0.0000000000 4.0182361526 4.0182361526 4.0182361526 0.0000000000" ;


In all the examples of this tutorial, we will be using the new structure input variable (added in version 9) to initialize the unit cell from an external file so that we don’t need to repeat the unit cell over and over again in all the input files. The syntax is :

structure "abifile:MgO_eph_zpr/flow_zpr_mgo/w0/t0/outdata/out_DEN.nc"


where the abifile prefix tells ABINIT that the lattice parameters and atomic positions should be extracted from an ABINIT binary file e.g. HIST.nc, DEN.nc, GSR.nc, etc. (other formats are supported as well, see the documentation).

To print the crystalline structure to terminal, use the abitk Fortran executable shipped with the ABINIT package and the crystal_print command:

abitk crystal_print MgO_eph_zpr/flow_zpr_mgo/w0/t0/outdata/out_DEN.nc

==== Info on the Cryst% object ====
Real(R)+Recip(G) space primitive vectors, cartesian coordinates (Bohr,Bohr^-1):
R(1)=  0.0000000  4.0182362  4.0182362  G(1)= -0.1244327  0.1244327  0.1244327
R(2)=  4.0182362  0.0000000  4.0182362  G(2)=  0.1244327 -0.1244327  0.1244327
R(3)=  4.0182362  4.0182362  0.0000000  G(3)=  0.1244327  0.1244327 -0.1244327
Unit cell volume ucvol=  1.2975866E+02 bohr^3
Angles (23,13,12)=  6.00000000E+01  6.00000000E+01  6.00000000E+01 degrees
Time-reversal symmetry is present
Reduced atomic positions [iatom, xred, symbol]:
1)    0.0000000  0.0000000  0.0000000  Mg
2)    0.5000000  0.5000000  0.5000000   O


This is the crystalline structure that we will be using in the forthcoming examples.

Since we want to compute the renormalization of the band gap due to phonons, it is also useful to have a look at the KS gaps obtained from the GS run. The command is:

abitk ebands_gaps MgO_eph_zpr/flow_zpr_mgo/w0/t0/outdata/out_DEN.nc

Direct band gap semiconductor
Fundamental gap:     4.479 (eV)
VBM:     4.490 (eV) at k: [ 0.0000E+00,  0.0000E+00,  0.0000E+00]
CBM:     8.969 (eV) at k: [ 0.0000E+00,  0.0000E+00,  0.0000E+00]
Direct gap:         4.479 (eV) at k: [ 0.0000E+00,  0.0000E+00,  0.0000E+00]


The same abitk command can be used with all netcdf files containing KS energies e.g GSR.nc, WFK.nc.

Warning

Our values for the gaps are consistent with the results for MgO given on the materialsproject. Remember, however, that the values and the positions of the gaps may vary (in somes cases even significantly) depending on the $\kk$-sampling.

In this case, abitk reports the gaps computed from a $\kk$-mesh as the DEN file can only be produced by a SCF calculation. The results for MgO are correct simply because the CBM/VBM are at the $\Gamma$ point and this point belongs to the GS $\kk$-mesh. Other systems (e.g. Si) may have the CBM/VBM at wavevectors that are not easily captured with a homogeneous mesh. The most reliable approach to find the location of the CBM/VBM is to perform a band structure calculation on a high-symmetry $\kk$-path.

abitk is handy if you need to call Fortran routines from the terminal to perform basic tasks but Fortran is not the best language when it comes to post-processing and data analysis. This kind of operation, indeed, is much easier to implement using a high-level language such as python. To plot the band structure using the GS eigenvalues stored in the GSR.nc file, use the abiopen script provided by AbiPy with the -e option:

abiopen.py MgO_eph_zpr/flow_zpr_mgo/w0/t1/outdata/out_GSR.nc -e


The figure confirms that the gap is direct at the $\Gamma$ point. The VBM is three-fold degenerate when SOC is not included.

## How to merge partial DDB files with mrgddb¶

First of all, let’s merge the partial DDB files with the command

mrgddb < teph4zpr_1.in


and the following input file:

that lists the relative paths of the partial DDB files in the MgO_eph_zpr directory.

Since we are dealing with a polar material, it is worth checking whether our final DDB contains Born effective charges and the electronic dielectric tensor. Instead of running anaddb or abinit and then checking the output file, we can simply use abiopen with the -p option:

abiopen.py MgO_eph_zpr/flow_zpr_mgo/w1//outdata/out_DDB -p

================================== DDB Info ==================================

Number of q-points in DDB: 8
guessed_ngqpt: [4 4 4] (guess for the q-mesh divisions made by AbiPy)
ecut = 35.000000, ecutsm = 0.000000, nkpt = 36, nsym = 48, usepaw = 0
nsppol 1, nspinor 1, nspden 1, ixc = 11, occopt = 1, tsmear = 0.010000

Has total energy: False, Has forces: False
Has stress tensor: False

Has (at least one) atomic pertubation: True
Has (at least one diagonal) electric-field perturbation: True
Has (at least one) Born effective charge: True
Has (all) strain terms: False
Has (all) internal strain terms: False
Has (all) piezoelectric terms: False


We can also invoke anaddb directly from python to have a quick look at the phonon dispersion:

abiview.py ddb MgO_eph_zpr/flow_zpr_mgo/w1//outdata/out_DDB

Computing phonon bands and DOS from DDB file with
nqsmall = 10, ndivsm = 20;
asr = 2, chneut = 1, dipdip = 1, lo_to_splitting = automatic, dos_method = tetra


that produces the following figures:

The results seem reasonable: the acoustic modes go to zero linearly for $\qq \rightarrow 0$ as we are dealing with a 3D system, no instability is present and the phonon dispersion shows the LO-TO splitting typical of polar materials.

Note, however, that the acoustic sum-rule is automatically enforced by the code so it is always a good idea to compare the results with/without asr as this is an indirect indicator of the convergence/reliability of our calculations. We can automate the process with the ddb_asr command of abiview :

abiview.py ddb_asr MgO_eph_zpr/flow_zpr_mgo/w1//outdata/out_DDB


that produces the following figure:

Important

This clearly indicates that the breaking of the acoustic sum-rule is not negligible. In this case, the breaking is mainly due the too low cutoff energy employed in our calculations. In real life, one should stop here and redo the DFPT calculation with a larger ecut and possibly a denser $\qq$-mesh but since the goal of this lesson is to teach you how to run ZPR calculations, we ignore this serious problem and continue with the other examples.

PS: If you want to compute the phonons bands with/without dipdip, use:

abiview.py ddb_dipdip MgO_eph_zpr/flow_zpr_mgo/w1//outdata/out_DDB


## How to merge partial POT files with mrgdv¶

Now we can merge the DFPT potential with the mrgdv tool using the command.

mrgdv < teph4zpr_2.in


and the following input file:

Tip

The number at the end of the POT file corresponds to the (idir, ipert) pertubation for that particular $\qq$-point. The pertcase index is computed as:

pertcase = idir + ipert


where idir gives the direction ([1, 2, 3]) and ipert specifies the perturbation type:

• ipert in [1, …, natom] corresponds to atomic perturbations (reduced directions)
• ipert = natom + 1 corresponds d/dk (reduced directions)
• ipert = natom + 2 corresponds the electric field
• ipert = natom + 3 corresponds the uniaxial stress (Cartesian directions)
• ipert = natom + 4 corresponds the shear stress. (Cartesian directions)

All DFPT POT files with 1 <= pertcase <= 3 x natom therefore correspond to atomic pertubations for a given $\qq$-point.

The value of pertcase and qpt are reported in the ABINIT header. To print the header to terminal, use abitk with the hdr_print command

 abitk hdr_print MgO_eph_zpr/flow_zpr_mgo/w1/t11/outdata/out_POT4.nc

===============================================================================
ECHO of part of the ABINIT file header

First record :

Second record :
bantot,intxc,ixc,natom  =   480     0    11     2
ngfft(1:3),nkpt         =    32    32    32    40
nspden,nspinor          =     1     1
nsppol,nsym,npsp,ntypat =     1    48     2     2
occopt,pertcase,usepaw  =     1     4     0
ecut,ecutdg,ecutsm      =  3.5000000000E+01  3.5000000000E+01  0.0000000000E+00
ecut_eff                =  3.5000000000E+01
qptn(1:3)               =  5.0000000000E-01  0.0000000000E+00  0.0000000000E+00
rprimd(1:3,1)           =  0.0000000000E+00  4.0182361526E+00  4.0182361526E+00
rprimd(1:3,2)           =  4.0182361526E+00  0.0000000000E+00  4.0182361526E+00
rprimd(1:3,3)           =  4.0182361526E+00  4.0182361526E+00  0.0000000000E+00
stmbias,tphysel,tsmear  =  0.0000000000E+00  0.0000000000E+00  1.0000000000E-02



Use --prtvol 1 to output more records.

Now we discuss in more detail the output file produced by mrgdv

For each $\qq$-point found in the partial POT files, the code prints a lists with the atomic perturbations that have been merged in the database.

 qpoint: [ 0.0000E+00,  0.0000E+00,  0.0000E+00] is present in the DVDB file
The list of irreducible perturbations for this q vector is:
1)  idir= 1, ipert=   1, type=independent, found=Yes
2)  idir= 2, ipert=   1, type=symmetric, found=No
3)  idir= 3, ipert=   1, type=symmetric, found=No
4)  idir= 1, ipert=   2, type=independent, found=Yes
5)  idir= 2, ipert=   2, type=symmetric, found=No
6)  idir= 3, ipert=   2, type=symmetric, found=No


The term symmetric means that this particular (idir, ipert) perturbation can be reconstructed by symmetry from the other independent entries with the same $\qq$-point. If all the independent entries are available, the code prints the following message at the end of the output file:

 All the independent perturbations are available
Done


Warning

If you don’t get this message, the DVDB cannot be used by the EPH code. In this case, check carefully your DFPT input files and the list of POT files that have been merged. Also, note that it is not possible to change the value of nsym at the level of the EPH calculation as symmetries are automatically inherited from the previous GS/DFPT calculations.

## Computing the WFK files with empty states¶

At this point we have all the ingredients (DDB and DVDB) required to compute/interpolate the e-ph scattering potentials and we can finally start to generate the WFK files.

For our first NSCF calculation, we use a 4x4x4 $\Gamma$-centered $\kk$-mesh and 70 bands so that we can perform initial convergence studies for the number of empty states in the self-energy. Then we generate WFK files with denser meshes and less bands that will be used for the Sternheimer method. Note the use of getden_filepath to read the DEN.nc file instead of getden or irdden.

You may now run the NSCF calculation by issuing:

abinit teph4zpr_3.in > teph4zpr_3.log 2> err &


with the input file given by:

At this point, it is worth commenting about the use of nbdbuf. As mentioned in the documentation, the highest energy states require more iterations to convergence. To avoid wasting precious computing time, we use a buffer that is ~10% of nband. This tricks significantly reduces the wall-time as the NSCF calculation completes when only the first nband - nbdbuf states are converged within tolwfr. Obviously, one should not use the last nbdbuf states in the subsequent EPH calculation. The same trick is highly recommended when computing WFK files for $GW$ calculations.

Important

For mobility calculations, it is possible to reduce significantly the cost of the WFK computation by restricting the NSCF calculation to the $\kk$-points inside the electron (hole) pockets relevant for transport. Unfortunately, this optimization is not possible when computing the real part of the self-energy as the integration must be performed in the full $\text{IBZ}_\kk$. On the other hand, ZPR calculations can take advange of the Sternheimer method to reduce the number of empty bands required to converge.

## Our first ZPR calculation¶

For our first example, we use a minimalistic input file so that we can discuss the most important input variables and the content of the main output file. First of all, you may want to start immediately the computation by issuing:

abinit teph4zpr_4.in > teph4zpr_4.log 2> err &


with the following input file:

Tip

To run the examples in parallel with e.g 2 MPI processes use:

mpirun -n 2 abinit teph4zpr_4.in > teph4zpr_4.log 2> err &


The EPH code will automatically distribute the workload using a predefined distribution scheme (not necessarily the most efficient in terms of memory and wall-time). In the last part of the tutorial, we explain how to specify a particular MPI distribution scheme with the eph_np_pqbks input variable.

Let’s now discuss the meaning of the different variables in more detail. We use optdriver 7 to enter the EPH code while eph_task 4 activates the computation of the full self-energy (real + imaginary parts). The paths to the external files (DDB, WFK, DVDB) are specified with the three variables:

This is an excerpt of the input file:

getddb_filepath "teph4zpr_1_DDB"

ddb_ngqpt 4 4 4  # The code expects to find in the DDB
# all the IBZ q-points corresponding to a 4x4x4 q-mesh

getdvdb_filepath "teph4zpr_2_DVDB"
getwfk_filepath "teph4zpr_3o_WFK"  # 4x4x4 k-mesh with 70 bands


The mesh for electrons (ngkpt, nshiftk and shiftk) must correspond to the one used for the input WFK file. ddb_ngqpt is set to 4x4x4 as this is the $\qq$-mesh used in the DFPT part to generate the DDB and DVDB files, but the integration in $\qq$-space is performed with the eph_ngqpt_fine mesh. As eph_ngqpt_fine differs from ddb_ngqpt, the code will automatically activate the interpolation of the DFPT potentials as discussed in the introduction to the EPH code. The $\qq$-space integration is defined by eph_intmeth and zcut.

We can now have a look at the main output file:

First of all, we find a section that summarizes the most important parameters:

 Number of bands in e-ph self-energy sum: 30
From bsum_start: 1 to bsum_stop: 30
Symsigma: 1 Timrev: 1
Imaginary shift in the denominator (zcut): 0.010 [eV]
Method for q-space integration:  Standard quadrature
Both Real and Imaginary part of Sigma will be computed.
Number of frequencies along the real axis: 0 , Step: 0.000 [eV]
Number of frequency in generalized Eliashberg functions: 0
Number of temperatures: 1 From: 0.000000E+00 to 0.000000E+00 [K]
Ab-initio q-mesh from DDB file: [4, 4, 4]
Q-mesh used for self-energy integration [ngqpt]: [8, 8, 8]
Number of q-points in the IBZ: 29
asr: 1 chneut: 1
dipdip: 1 symdynmat: 1
Number of k-points for self-energy corrections: 1
Including all final {mk+q} states inside energy window: [4.347 9.112 ] [eV]
List of K-points for self-energy corrections:
1     1  [ 0.0000E+00,  0.0000E+00,  0.0000E+00]   6    9


Note

Note how the acoustic sum-rule (asr), the charge neutrality of the Born effective charges (chneut), the treatment of dipole-dipole interaction in the dynamical matrix (dipdip), are activated by default in v9.

Then we find another section related to MPI parallelism. In this case we are running in sequential but the output will change if we run in paralle (see also eph_np_pqbks). The final message informs the user that the EPH code will either read the qpts from file (if the DVDB contains all of them, in case eph_ngqpt_fine is not defined in the input) or interpolate the scattering potentials from ddb_ngqpt to eph_ngqpt_fine.

 === MPI parallelism ===
P Allocating and summing bands from my_bsum_start: 1 up to my_bsum_stop: 30
P Number of CPUs for parallelism over perturbations: 1
P Number of perturbations treated by this CPU: 6
P Number of CPUs for parallelism over q-points: 1
P Number of q-points in the IBZ treated by this proc: 29 of 29
P Number of CPUs for parallelism over bands: 1
P Number of CPUs for parallelism over spins: 1
P Number of CPUs for parallelism over k-points: 1
P Number of k-point in Sigma_nk treated by this proc: 1 of 1

 DVDB file contains all q-points in the IBZ --> Reading DFPT potentials from file.


or

 Cannot find eph_ngqpt_fine q-points in DVDB --> Activating Fourier interpolation.


Finally, we have the section with the QP results for each spin, $\kk$-point and temperature, followed by the value of the direct gap computed with the two approaches (linearized QP equation and OTMS):

================================================================================
Final results in eV.
Notations:
eKS: Kohn-Sham energy. eQP: quasi-particle energy.
eQP - eKS: Difference between the QP and the KS energy.
SE1(eKS): Real part of the self-energy computed at the KS energy, SE2 for imaginary part.
Z(eKS): Renormalization factor.
FAN: Real part of the Fan term at eKS. DW: Debye-Waller term.
DeKS: KS energy difference between this band and band-1, DeQP same meaning but for eQP.
OTMS: On-the-mass-shell approximation with eQP ~= eKS + Sigma(omega=eKS)
TAU(eKS): Lifetime in femtoseconds computed at the KS energy.
mu_e: Fermi level for given (T, nelect)

K-point: [ 0.0000E+00,  0.0000E+00,  0.0000E+00], T:    0.0 [K], mu_e:    7.721
B    eKS     eQP    eQP-eKS   SE1(eKS)  SE2(eKS)  Z(eKS)  FAN(eKS)   DW      DeKS     DeQP
6   4.490    4.563    0.073    0.089   -0.002    0.820   -0.301    0.390    0.000    0.000
7   4.490    4.563    0.073    0.089   -0.002    0.820   -0.301    0.390    0.000    0.000
8   4.490    4.563    0.073    0.089   -0.002    0.820   -0.301    0.390    0.000    0.000
9   8.969    8.886   -0.083   -0.085   -0.000    0.981    0.117   -0.201    4.479    4.323

KS gap:    4.479 (assuming bval:8 ==> bcond:9)
QP gap:    4.323 (OTMS:    4.305)
QP_gap - KS_gap:   -0.156 (OTMS:   -0.174)


The calculation has produced the following output files:

$ls teph4zpr_4o_* teph4zpr_4o_EBANDS.agr teph4zpr_4o_PHBANDS.agr teph4zpr_4o_PHDOS.nc teph4zpr_4o_PHBST.nc teph4zpr_4o_SIGEPH.nc  where: • EBANDS.agr → Electron bands in xmgrace format. See also prtebands • PHBST.agr → Phonon bands in xmgrace format. See also prtphbands ph_ndivsm, ph_nqpath, and ph_qpath. • PHDOS.nc → Phonon DOS in netcdf format (see prtphdos is given by ph_ngqpt). • PHPBST.nc → Phonon band structure in netcdf format • SIGEPH.nc → Netcdf file with $\Sigma^{\text{e-ph}}$ results. All the QP results are stored in the SIGPEPH.nc netcdf file for all $\kk$-points, spins and temperatures. As usual, one can use abiopen with the -p option (--print) to print a summary to terminal: $ abiopen.py teph4zpr_4o_SIGEPH.nc -p

============================ SigmaEPh calculation ============================
Calculation type: Real + Imaginary part of SigmaEPh
Number of k-points in Sigma_{nk}: 1
sigma_ngkpt: [0 0 0], sigma_erange: [0. 0.]
Max bstart: 5, min bstop: 9
Initial ab-initio q-mesh:
ngqpt: [4 4 4], with nqibz: 8
q-mesh for self-energy integration (eph_ngqpt_fine): [4 4 4]
k-mesh for electrons:
mpdivs: [4 4 4] with shifts [0. 0. 0.] and kptopt: 1
Number of bands included in e-ph self-energy sum: 30
zcut: 0.00037 (Ha), 0.010 (eV)
Number of temperatures: 4, from 0.0 to 300.0 (K)
symsigma: 1
Has Eliashberg function: False
Has Spectral function: False

Printing QP results for 2 temperatures. Use --verbose to print all results.

KS, QP (Z factor) and on-the-mass-shell (OTMS) direct gaps in eV for T = 0.0 K:
Spin  k-point                              KS_gap    QPZ_gap    QPZ - KS    OTMS_gap    OTMS - KS
------  ---------------------------------  --------  ---------  ----------  ----------  -----------
0  [+0.000, +0.000, +0.000] $\Gamma$     4.479      4.323      -0.156       4.305       -0.174

KS, QP (Z factor) and on-the-mass-shell (OTMS) direct gaps in eV for T = 300.0 K:
Spin  k-point                              KS_gap    QPZ_gap    QPZ - KS    OTMS_gap    OTMS - KS
------  ---------------------------------  --------  ---------  ----------  ----------  -----------
0  [+0.000, +0.000, +0.000] $\Gamma$     4.479      4.288      -0.191       4.262       -0.217


where QPZ stands for the results obtained with the linearized QP equation and OTMS are the results with the on-the-mass-shell approach. We can also plot the most important results by just replacing -p with -e (--expose):

\$ abiopen.py teph4zpr_4o_SIGEPH.nc -e


that produces:

On the left, we have the QP direct gap at $\Gamma$ ad a function of T while the OTMS results are shown in the right panel. In both cases, the QP direct band gap decreases with increasing temperature (remember that MgO is a direct gap semiconductor). This is the so-called Varshni’s effect that is observed in many (but not all) semiconductors.

The ZPR computed with the two approaches differ by ~26 meV but this is expected as the linearized QP equations reduces to the OTMS only if the $Z$ factor is very close to one or if there is some sort of fortuitous cancellation between the CBM/VBM corrections. In our calculation, the Z factor for the VBM is 0.644 while for the CBM we obtain Z = 0.963. These values are reasonable, still it’s not so uncommon to obtain unphysical Z factors in e-ph calculations i.e. values > 1, especially for states far from the band edge as the e-ph self-energy has a lot of structure in frequency-space and the linearized QP approach is not always justified. For this reason, in the rest of the tutorial, we will be focusing on the analysis of the OTMS results.

Important

To compute the imaginary part of $\Sigma^{\text{e-ph}}$ at the KS energy, we strongly recommend to use eph_task -4 as this option activates several important optimizations that are not possible when the full self-energy is wanted. Note, however, that eph_task -4 is not able to provide the full frequency dependence, only the value of the imaginary part at the KS eigenvalue. The computation of spectral functions and Eliashberg functions therefore requires eph_task +4.

## Convergence w.r.t. nband¶

At this point it should not be so difficult to write an input file to perform ZPR calculations for different values of nband for a fixed $\qq$-mesh. It is just a matter of adding the following set of variables to the previous input file:

ndtset 3
nband: 20
nband+ 20


An example is given in

Run the calculation, as usual, using

abinit teph4zpr_5.in > teph4zpr_5.log 2> err &


Now we can extract the results from the output file

or, alternatively, use the AbiPy abicomp script to post-process the results stored in the SIGPEPH files:

abicomp.py sigeph teph4zpr_5o_DS*_SIGEPH.nc -e


The figure on the left shows the convergence of the OTMS ZPR as a function of nband. The figure on the right, gives the correction to the KS band gas as a function of $T$ obtained for different numbers of bands. A similar behaviour is observed also for the linearized equation.

The results are quite disappointing in the sense that the QP gaps are far from being converged and the convergence rate is even worse if we look at the QP energies (the same behaviour is observed in $GW$). A more careful (and expensive) convergence study would reveal that 300 bands are needed. This computation, although feasible, would be too costly for a tutorial and is therefore left as an extra exercise. In the next section, we will see that the EPH code provides a much more efficient algorithm to accelerate the convergence.

Exercise

Change the input file to use mixprec 1 and boxcutmin 1.1. Rerun the calculation and compare with the previous results. Do you see significant differences? What about the wall-time and the memory allocated for $W(\rr, \RR)$ Hint: use

grep log <<< MEM


to extract the lines where the most imporant memory allocation are reported.

## How to reduce the number of bands with the Sternheimer method¶

To activate the Sternheimer approach, we need to set eph_stern to 1 and use getpot_filepath to specify the external file with the GS KS potential. This file is produced at the end of the GS calculations provided prtpot is set to 1 in the input file.

Important

The Sternheimer equation is solved non-self-consistently using max nline NSCF iterations and the solver stops when the first-order wavefunction is converged within tolwfr. Default values for these two variables are provided if not specified by the user in the input file. The code aborts if the solver cannot converge. This may happen if the first nband states are not well separated from the remaining high-energy states. Increasing nband should solve the problem.

An example of input file is provided in:

and can be run with:

abinit teph4zpr_6.in > teph4zpr_6.log 2> err &


Four calculations are performed with different values of nband:

ndtset 4
nband: 10
nband+ 10


in order to monitor the convergence of the QP corrections.

To analyze the convergence behavior, we can extract the results from the main output file

or, alternatively, we can pass the list of SIGEPH files to the abicomp script and use the sigpeh command:

abicomp.py sigeph teph4zpr_6o_DS*_SIGEPH.nc -e


Now the convergence is much better. Note the different scale on the y-axis.

Important

The big advange of the Sternheimer method is that we don’t need to compute WFK files with many empty bands in order to converge the real part of the self-energy. This means that one can use the computing power to densify the $\kk$-mesh while keeping the number of empty states at a reasonable level. Producing a WFK file with 1000 bands and a $100^3$ $\kk$-mesh is indeed way more expensive than performing the same computation with, let’s say, 50 bands.

Note, however, that the cost of the Sternheimer method quickly increases with nband due to the orthogonalization process. This means that a ZPR calculation with 300 bands (occ + empty) without the Sternheimer method is much faster than the same computation done with eph_stern 1. As a matter of fact, we use the Sternheimer method so that we don’t need 300 bands to convergence the results.

Exercise

Generate a new WFK with nband ~ %mpw and run a ZPR calculation without the Sternheimer method. Compare the sum-over-states results with the Sternheimer ones. Why it is not a good idea to use nband >= mpw?

## Convergence of the ZPR w.r.t. the q-mesh¶

Now we fix this value of nband to 20 and perform a convergence study for the $\qq$-sampling. Unfortunately, we won’t be able to fully convergence the results but, at least, you will get an idea on how to perform this kind of convergence study.

In the input teph4zpr_3.in, we have computed WFK files on different $\kk$-meshes and a relatively small number of empty states (25 - 5 = 20). We can finally use these WFK files to perform ZPR calculations by just adding:

ndtset 3
nband 20

ngkpt1 4 4 4    eph_ngqpt_fine1 4 4 4    getwfk_filepath1 "teph4zpr_3o_DS1_WFK"
ngkpt2 8 8 8    eph_ngqpt_fine2 8 8 8    getwfk_filepath2 "teph4zpr_3o_DS2_WFK"
ngkpt3 12 12 12 eph_ngqpt_fine3 12 12 12 getwfk_filepath3 "teph4zpr_3o_DS3_WFK"

eph_stern 1
getpot_filepath "MgO_eph_zpr/flow_zpr_mgo/w0/t0/outdata/out_POT.nc"


Run the calculation, as usual, with:

abinit teph4zpr_7.in > teph4zpr_7.log 2> err &


The output file is reported here for your convenience:

Now use:

abicomp.py sigeph teph4zpr_7o_DS*_SIGEPH.nc -e


to analyze the convergence with the $\qq$-mesh:

The convergence of the ZPR is shown in the left panel while the $T$-dependent band gaps obtained with different $\qq$-meshes are reported in the right panel.

Obviously we are still far from convergence but a 12x12x12 $\qq$-mesh is the best we can afford in a tutorial. It is clear that one should test denser $\qq$-meshes and, last but not least, monitor the behaviour for zcut → 0. These additional convergence tests cannot be covered in this lesson and they are left as exercises.

## How to compute the spectral function¶

To compute the spectral function, we need to specify the number of frequencies via nfreqsp. The code will compute $A_\nk(\ww)$ on a linear frequency mesh centered on the KS eigenvalue that spans the interval $[\ee_\nk - \Delta, \ee_\nk + \Delta]$ with $\Delta$ given by freqspmax. An odd number of frequency points is enforced by the code.

In a nutshell, one should add e.g.:

nfreqsp 301
freqspmax 1.0 eV


An example of input file is available here:

Execute it with:

abinit teph4zpr_8.in > teph4zpr_8.log 2> err &


To plot the spectral function $A_\nk(\ww)$ in an easy way, use AbiPy to extract the data from the netcdf file. We can do it in two different ways:

• using a small python script that calls the AbiPy API
• using abiopen to interact with the netcdf file inside ipython

The python script is reported here:

Script to plot $A_\nk(\ww)$
#!/usr/bin/env python

import sys
from abipy.abilab import abiopen

# Get file name from command line and open the file
filepath = sys.argv[1]
abifile = abiopen(filepath)

# Plot Sigma(omega), A(omega) and QP solution
abifile.plot_qpsolution_skb(spin=0, kpoint=[0, 0, 0], band=7)


Alternatively, one can use

abiopen.py teph4zpr_8o_DS1_SIGEPH.nc


to open the SIGEPH.nc file inside ipython. Then, inside the ipython terminal, issue:

%matplotlib

abifile.plot_qpsolution_skb(spin=0, kpoint=[0, 0, 0], band=7)


to plot $\Sigma_\nk(\ww)$ and $A_\nk(\ww)$ at the $\Gamma$ point for band index 7 (conduction band maximum). Note that in python indices start from 0 whereas ABINIT uses Fortran conventions with indices starting from 1. In a nutshell, one should substract 1 from the Fortran band index when calling AbiPy functions.

Some comments are in order here.

(i) The intersection between the blue solid and dashed lines gives the graphical solution of the non-linear QP equation:

\ee^\QP_\nk = \ee^\KS_\nk + \Sigma_\nk^{\text{e-ph}}(\ee^\QP_\nk)

restricted to the real axis whereas the blue circle represents the solution obtained with the linearized QP equation with the renormalization factor $Z$. For this particular state, the real part of $\Sigma$ is almost linear around $\ee^\KS_\nk$ and the linearized solution is very close to the non-linear one. There may be cases, however, in which the real part is highly non-linear and the two approaches will give different answers.

(ii) The QP solution is located inside the KS gap where $\Im\Sigma$ is zero (well, strictly speaking, it is very small yet finite because we are using a finite zcut in the calculation). As a net result, $A_\nk(\ww)$ presents a sharp peak in correspondence of the QP energy. Note also that our computed $A_\nk(\ww)$ does not integrate to 1 because the number of points nfreqsp is too small and part of the weight is lost in the numerical integration. This can be easily fixed by increasing the resolution of the frequency mesh.

(iii) For this particular $\nk$ state, $\ww - \ee^0$ has a single intersection with $\Re \Sigma_\nk(\ww)$. However, it is possible to have configurations with multiple intersections that may lead to some background and/or additional satellites in $A_\nk(\ww)$ provided the imaginary part of $\Sigma_\nk(\ww)$ is sufficiently small in that energy range.

(iiii) The vertical red line in the second figure gives the on-shell QP energy. As already mentioned previously, the OTMS energy derives from a static formalism that reduces to the linearized QP result only if $Z = 1$ so it is not surprising to see that the QP peak differs from the OTMS energy. Still, this does not mean that the OTMS is wrong. Actually, it is more puzzling the fact that the background in $A_\nk(\ww)$is strongly suppressed and no phonon satellite is clearly visible. Obviously, our entire discussion is based on completely underconverged results but the more careful investigation reported in [Nery2018] indicates that an accurate description of dynamical effects requires the (approximate) inclusion of additional Feynmann diagrams through the cumulant expansion and that the OTMS approximation gives QP energies that are much closer to the QP peak obtained with the cumulant method.

This (lengthy) discussion was somehow needed to justify the reason why we have been focusing on the OTMS results in most of this tutorial. Now we focus on more technical aspects and, in particular, on how to compare spectral functions and self-energies obtained with different settings.

Remember that in teph4zpr_8.in we computed $A_\nk(\ww)$ with/without the Sternheimer method. So the question is “how can we compare the two calculations and what can we learn from this analysis?”

To compare the results obtained with/wo the Sternheimer, use abicomp

abicomp.py sigeph teph4zpr_8o_DS*_SIGEPH.nc


and then, inside ipython, type:

%matplotlib

robot.plot_selfenergy_conv(spin=0, kpoint=0, band=7)


to produce the figure below with the real part, the imaginary part of the self-energy and the spectral function obtained with the different approaches:

Note the following:

(i) The imaginary part of $\Sigma_\nk(\ww)$ is not affected by the Sternheimer method as long as we explicitly include enough nband bands around the $\nk$ state to account for phonon absorption/emission with the full dynamical self-energy.

(ii) The real part of $\Sigma_\nk(\ww)$ obtained with the two methods differ but the results should be interpreted with a critical eye. The Sternheimer method, indeed, is designed to accelerate the convergence of $\Sigma_\nk(\ee^\KS_\nk)$ w.r.t. nband but cannot exactly reproduce the behaviour of $\Sigma_\nk(\ww)$ at large frequencies since it is a static approximation. In other words, once the two approaches are properly converged one should see that the real part of the two self-energies agree around the bare KS eigenvalue and that the two curves stars to deviate at large $\ww$. This test is left as an additional excercise for volunteers.

For additional examples for Diamond, see this jupyter notebook

## MPI parallelism and memory requirements¶

There is an important difference with respect to eph_task -4 (computation of the imaginary part) that is worth discussing in more detail. When computing the imaginary part at the KS energy for transport properties, the EPH code is able to filter both $\kk$- and $\qq$-points so that only the relevant states around the band edge(s) are stored in memory. Unfortunately, in the case of full self-energy calculations, this filtering algorithm is not possible and each MPI process needs to store all the $\kk$-wavevectors the IBZ to be able to compute the e-ph matrix elements connecting $\kk$ to $\kq$. In other words, the wavefunctions in the IBZ are not MPI-distributed and this leads to a significant increase in the memory requirements, especially for dense meshes and/or large nband. Fortunately, the code is able to distribute bands among the MPI processes in the band communicator hence the memory required for the wavefunctions will scale as nband / np_band where np_band is the number of MPI processes in the band communicator (see eph_np_pqbks).

For ZPR calculations, the priorities are as follows:

1. Use enough np_band MPI processes to decrease the memory for the wavefunctions. Ideally, np_band should divide nband to distribute the work equally. The maximum number of MPI procs that can used for this level is nband but let’s not exaggerate. Keep some procs for the other MPI levels that are usually more efficient in terms of wall-time.

2. Once the memory for the wavefunctions reaches a reasonable level, activate the parallelism over perturbations to decrease the memory for $W(\rr, \RR, 3\times\text{natom})$. For better efficiency, eph_nperts should divide 3 * natom. As explained in the introduction page for the EPH code, this MPI level allows one to reduce the memory allocated for $W(\rr, \RR, 3\times\text{natom})$ so the number of procs in this communicator should divide 3 * natom.

3. If the memory for the wavefunctions and $W$ is under control, you may want to activate the $\qq$-point parallelism to speedup the calculation. The memory requirements will not decrease, though.

4. Finally, use the $\kk$-point parallelism if there are enough CPUs available to boost the calculation. Obviously, this kind of parallelism makes sense if you are computing QP corrections for several $\kk$ points and the number of np_kpt MPI processes should be adjusted accordingly. Keep in mind, however, that each $\kk$-point has a different computational cost so load imbalance is expected.

Important

Finally, remember that setting boxcutmin to a value smaller than 2 (e.g. 1.1) leads to a significant decrease in the memory required to store $W$ while mixprec = 1 allows one to decrease the computational cost of the FFTs.