mirror of https://github.com/abinit/abinit.git
Minor fixes
This commit is contained in:
gmatteo
parent
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@ -45,7 +45,7 @@ Tested with __CentOS 8.2__
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- [NetCDF](https://www.unidata.ucar.edu/software/netcdf): Network Common Data Form
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- [libXC](https://tddft.org/programs/libxc/download/): Library of exchange-correlation functionals
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6. __Optional supported librairies:__
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6. __Optional libraries:__
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- [FFTW3](http://www.fftw.org/): Library for computing the discrete Fourier transform, **recommended with GNU**
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- [libxml2](http://xmlsoft.org/downloads.html): XML C parser, recommended for multibinit
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@ -65,11 +65,11 @@ __a relatively simple parallel version of ABINIT__ are summarized below:
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`sudo dnf install gcc-gfortran`
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2. __Install MPI library__ (MPICH)
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2. __Install the MPI library__ (MPICH)
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`sudo dnf install mpich mpich-devel`
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3. __Install linear algebra library__ (OpenBLAS)
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3. __Install the linear algebra library__ (OpenBLAS)
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`sudo dnf install openblas`
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@ -85,7 +85,7 @@ __a relatively simple parallel version of ABINIT__ are summarized below:
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`sudo dnf install fftw fftw-devel`
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6. __Install python interpreter__
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6. __Install the python interpreter__
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`sudo dnf install python3`
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@ -209,8 +209,8 @@ All the variables and flags supported by the script can be found by typing:
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Some options are detected automatically by the script.
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For example, with the option `--with-mpi="yes"`, ABINIT will try to use the parallel fortran compiler
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found in $PATH (e.g. mpifort) and will try to detect the directories containing the associated libraries
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and the header files required by MPI.
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found in $PATH (e.g. mpifort) and will try to detect the directories containing the libraries
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and the associated header files required by MPI.
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When a lot of options are needed, it is advised to use a config file.
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@ -360,7 +360,7 @@ sudo dnf install libxc libxc-devel
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sudo dnf install python3
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```
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### Getting sources of ABINIT
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### Getting the ABINIT tarball
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```sh
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wget https://www.abinit.org/sites/default/files/packages/abinit-9.0.4.tar.gz
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@ -440,7 +440,7 @@ eb ABINIT-9.1.0-foss-2019b.eb -Dr
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In the previous example we used one of the official easyconfig files shipped with the EasyBuild package.
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There are cases, however, in which we need to perform a customized build.
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For example, we may want to compile the latest stable version of ABINIT recently released,
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or use a different release, or maybe we just need to activate configuration options that are not present in the official file.
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or use a different version, or maybe we just need to activate configuration options that are not present in the official file.
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In this case, we can simply copy the original easyconfig file,
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change it according to our needs and finally pass this customized file to the `eb` script.
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@ -540,15 +540,3 @@ Changing `dependencies` and `configopts` requires some basic understanding of th
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and of the logic used by EasyBuild to automate the compilation.
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If you need a specialized `eb` file, feel free to contact the ABINIT developers on the forum or the EasyBuild
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developers to ask for support.
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<!--
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Unfortunately, we cannot cover all the possible scenarios so we focus on the simplest case in which
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we want to **extend** the recipe that is we just want to activate support for an optional library
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without changing the other easyconfigs parameters.
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Let's assume, for instance, that we want to build an Abinit version with Wannier90 support.
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Expert users may want to activate support for optional features that require additional external libraries
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e.g. wannier90, atompaw, etc.
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In this case, one has to list the external library in the `dependencies` section, add the correct configuration
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options to `configopts` and make sure that `eb` can find an easyconfig file that can be used to build the library.
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-->
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@ -36,16 +36,16 @@ If, on the other hand, you are not interested in compiling all the components fr
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you may want to consider the following alternatives:
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* Compilation with external libraries provided by apt-based Linux distributions (e.g. **Ubuntu**).
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More info available [here](INSTALL_Ubuntu).
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More info available [here](../INSTALL_Ubuntu).
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* Compilation with external libraries on **Fedora/RHEL/CentOS** Linux distributions.
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More info available [here](INSTALL_CentOS).
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More info available [here](../INSTALL_CentOS).
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* Homebrew bottles or macports for **MacOsX**.
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More info available [here](INSTALL_MacOS).
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More info available [here](../INSTALL_MacOS).
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* Automatic compilation and generation of modules on clusters with **EasyBuild**.
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More info available [here](INSTALL_EasyBuild).
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More info available [here](../INSTALL_EasyBuild).
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* Compiling Abinit using the **internal fallbacks** and the *build-abinit-fallbacks.sh* script
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automatically generated by *configure* if the mandatory dependencies are not found.
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@ -5,7 +5,7 @@ authors: GB, MG
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# Phonon-limited mobility
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This tutorial shows how to compute phonon-limited carrier mobilities in semiconductors within
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the relaxation time approximation (RTA), taking the specific case of AsAs as an example.
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the relaxation time approximation (RTA), taking the specific case of AlAs as an example.
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It is assumed the user has already completed the two tutorials [RF1](rf1) and [RF2](rf2),
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and that he/she is familiar with the calculation of ground state and response properties,
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in particular phonons, Born effective charges and dielectric tensor.
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@ -288,7 +288,6 @@ The file *\$ABI_TUTORESPFN/Input/teph4mob_4.in* is an example of such computatio
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It consists of two parts: the first one (dataset 1) computes the GS wavefunctions,
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and the second one (datasets 2-3) computes the dense WFK that will be used to evaluate the mobility.
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We also compute a denser WFK file that will be used with the double-grid method explained later.
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As we want to compute the mobility of electrons in the conduction band,
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we need to include enough conduction bands in the computation of the WFK ([[nband]] = 8).
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@ -307,8 +306,7 @@ abinit teph4mob_4.in > teph4mob_4.log 2> err &
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## Calculation of the mobility
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The computation of the mobility requires different convergence studies.
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We will explain them and their need in the following.
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Let us first explain the different parameters in a standard mobility computation.
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Let us first explain the different parameters required for a standard mobility computation.
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The file *\$ABI_TUTORESPFN/Input/teph4mob_5.in* is an example of such computation.
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{% dialog tests/tutorespfn/Input/teph4mob_5.in %}
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@ -323,12 +321,6 @@ getddb_filepath "teph4mob_2_DDB"
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getdvdb_filepath "teph4mob_3_DVDB"
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```
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<!--
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In this tutorial, the prefix of the external file is constructed from the name of the input file
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that produced the file and the dataset index.
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In real life, you may want to use more explicitative names such as 444_DDB etc.
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-->
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Now copy the input file in the *Work_eph4mob* directory, and run the code with:
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```sh
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@ -348,15 +340,16 @@ Let's discuss the meaning of the e-ph variables in more details:
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The code aborts with an error if [[ngkpt]] is not the same as the one found in the
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input WFK file. At present, multiple shifts ([[nshiftk]] > 1) are not supported.
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* [[occopt]] 3 is required to correctly compute the
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location of the Fermi level using the Fermi-Dirac occupation function as we are dealing with the
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physical temperature and not a fictitious broadening for integration purposes.
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* [[ddb_ngqpt]] defines the initial $\qq$-grid used for the DFPT computation (4×4×4 in this example)
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* [[eph_ngqpt_fine]] defines the dense $\qq$-mesh where the scattering potentials are interpolated
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and the e-ph matrix elements are computed.
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* We work within the rigid band model and introduce a small electron-doping: [[eph_doping]] = -2.26e+16
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that corresponds to [[eph_extrael]] 1e-6.
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Now how to set [[occopt]] to 3 to correctly compute the
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location of the Fermi level using the Fermi-Dirac occupation function as we are dealing with the
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physical temperature and not a fictitious broadening for integration purposes.
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!!! warning
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@ -402,6 +395,7 @@ we suggest to use a very small number, for instance $10^{15}$ to $10^{18}$ elect
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For the initial convergence studies, we suggest to start from a relatively small number
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of temperatures **covering the range of interest**.
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The T-mesh can be densified aftwerwards while keeping the same range once converged parameters are found.
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Note also that transport properties at low temperatures are much more difficult to converge as the
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derivative of the Fermi-Dirac distribution is strongly peaked around the Fermi level and hence
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a very dense sampling is needed to convergence the BZ integrals.
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@ -410,7 +404,7 @@ we suggest to use a very small number, for instance $10^{15}$ to $10^{18}$ elect
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The [[sigma_erange]] variable defines the energy window, below the VBM and above the
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CBM where the lifetimes will be computed.
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Since the BTE contains a derivative of the Fermi-Dirac occupation function centered on the Fermi level,
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Since the BTE contains the derivative of the Fermi-Dirac occupation function centered on the Fermi level,
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it is possible to filter the $\kk$-points that will contribute to the mobility and compute
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the lifetimes for these $\kk$-points only.
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The value of the derivative, indeed, decreases rapidly
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@ -425,8 +419,8 @@ Hopefully, in the next version this parameter will be automatically computed by
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We can now examine the log file in detail.
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After the standard output of the input variables, the code reports the different parameters
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used for the treatment of the long-range part of the DFPT potentials:
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the Born effective charges, the dielectric constant, and the quadrupolar tensor.
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Make sure to have all of them in order to have an
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the Born effective charges, the high-frequency dielectric constant and the dynamical quadrupole tensor.
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Make sure to have all of them in order to obtain an
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accurate interpolation of the scattering potentials, see discussion in [[cite:Brunin2020]].
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!!! important
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@ -524,10 +518,10 @@ Note that the transport driver is automatically executed after the EPH run.
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You can also run the transport driver in standalone mode by setting [[eph_task]] 7,
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provided you already have the lifetimes in an external SIGEPH.nc file that be specified via [[getsigeph_filepath]].
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This task is relatively fast even in serial execution although some parts
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(in particular the computation of DOS-like quantities) can benefit from MPI parallelism.
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(in particular the computation of DOS-like quantities) can benefit from MPI.
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Now that you know how to obtain the mobility in a semiconductor for given $\kk$- and $\qq$-meshes,
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we can give more details about convergence studies and discuss additional tricks that can be exploited
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Now that we know how to obtain the mobility in a semiconductor for given $\kk$- and $\qq$-meshes,
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we can give more details about convergence studies and discuss additional tricks
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to decrease significantly the computational cost.
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### Convergence w.r.t. the energy range
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@ -703,8 +697,9 @@ Just set [[eph_restart]] to 1 in the input file and rerun the job
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!!! important
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There is no harm in setting [[eph_restart]] to 1 from the begining but keep in mind that
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the code will overwrite the SIGEPH.nc if the file is completed so we do not recommended
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to use this option in MultiDataset mode.
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the code will restart the calculation from scratch if all the $\kk$-points in the SIGEPH.nc have
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been computed (a backup copy is kept).
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So we do not recommended the use of this option in MultiDataset mode.
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### Transport calculation from SIGEPH.nc
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@ -743,24 +738,30 @@ and you want to boost the calculation.
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### How to reduce the memory requirements
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As mentioned above, the memory should scale with the number of MPI processors used for the $\qq$-point and
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the perturbation distribution.
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the perturbation communicators.
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However, there might be tricky systems in which you start to experience memory shortage that
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prevents you from running with several MPI processes.
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prevents you from running with many MPI processes.
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This problems should show up for very dense $\kk$/$\qq$ meshes.
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As a rule of thumb, calculations with meshes denser than e.g 200x200x200 start to be very memory demanding
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and become much slower because several algorithms and tables related to the BZ sampling will start to dominate.
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As a rule of thumb, mobility calculations with meshes denser than e.g 200x200x200 start to be very memory demanding
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and the execution will slow down because several algorithms and internal tables for the BZ sampling
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and the tetrahedron method start to dominate.
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The double grid technique helps mitigate this bottleneck.
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In some cases, you may try to reduce slightly the value of [[sigma_erange]] to reduce the memory requirements.
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The code allocates a relatively small buffer to store the Bloch states involved in transport but unfortunately
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the $\kk$-points are not easy to distribute with MPI.
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To reduce the size of this part, one may opt for an internal buffer in single precision.
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This option is enabled by using `enable_gw_dpc="no"` at configure time (note that this is the default behaviour).
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Note also that the EPH code allocates a relatively small buffer to store the Bloch states involved
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in transport calculations but unfortunately the $\kk$-points are not easy to distribute with MPI.
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Moreover the size of this array depends on the electronic dispersion:
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systems with several relatively flat bands around the band edges require more memory.
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To reduce the memory for the wavefunctions, the code uses internal buffers in single precision.
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This option is enabled at configure time by using `enable_gw_dpc="no"` (this is the default behaviour).
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If these tricks do not solve your problem, consider using OpenMP threads.
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The code is not optimized for OpenMP but a few threads may be useful to avoid replicating memory at the MPI level.
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As a rule of thumb, 2-4 OpenMP threads should be OK provided you link with threaded FFT and BLAS libraries.
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Last but not least, do not use datasets: large arrays allocated for $\kk$-points and the size depends on [[ndtset]].
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Never ever use multiple datasets for big EPH calculations. You have been warned!
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Last but not least, **do not use datasets**: split the calculation into different input files
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and optimize the number of MPI processes according to the dimension of the problem.
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You have been warned!
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### How to compute only the k-points close to the band edges
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@ -1093,11 +1093,19 @@ abifile.plot_qpsolution_skb(spin=0, kpoint=[0, 0, 0], band=4)
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```
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Alternatively, one can directly use `abiopen.py` to open the SIGEPH.nc file inside the iptyhon terminal
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Execute:
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```
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abiopen.py teph4zpr_8o_DS1_SIGEPH.nc
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```
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to open the file in the ipython shell and then issue:
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```ipython
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%matplotlib
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abifile.plot_qpsolution_skb(spin=0, kpoint=[0, 0, 0], band=4)
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abifile.plot_qpsolution_skb(spin=0, kpoint=[0, 0, 0], band=5)
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```
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The advantage of the second approach is that you can interact with the python object in an interactive environment.
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@ -135,9 +135,9 @@ In this introduction, we focus on the parts that are common to the different sub
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The use of the different sub-drivers is discussed in more detail in the specialized lessons:
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<!--
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* [Phonon-limited mobilities](eph4mob)
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* [ZPR and T-dependent band structures](eph4zpr)
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<!--
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* [Isotropic superconductivity in metals](eph4isotc)
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-->
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@ -162,6 +162,8 @@ More specifically, in EPH the name of the DDB file is specified by
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present due to numerical inaccuracies) is reasonable.
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By the same token, make sure that no vibrational instabilty is present before
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embarking on big EPH calculations.
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If the spectrum present instabilities around $\Gamma$ due to a Fourier interpolation
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done with coarse $\qq$-sampling, you may try to use [[rifcsph]].
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### Variables for phonon DOS
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@ -458,9 +460,9 @@ to the DFPT computation.
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Since the EPH code does not need the first order change of the wavefunctions (1WF files)
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we suggest to avoid the output of these files by using [[prtwf]] = -1 in the DFPT part
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as these files are quite large and the overall space on disk scales as **nqpt × 3 × natom**.
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When [[prtwf]] is set to -1, the DFPT code writes the 1WF only if the DFPT SCF cycle is not converged
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When [[prtwf]] is set to -1, the DFPT code writes the 1WF file only if the DFPT SCF cycle is not converged
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so that one can still restart from these wavefunctions if needed
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(restarting a DFPT run from the 1WF file is more effective than restarting from the first order density).
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(restarting a DFPT run from the first order wavefunctions is more effective than restarting from the first order density).
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## Star-function interpolation of the KS eigenvalues
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@ -582,7 +584,7 @@ abitk skw_compare IBZ_GSR.nc KPATH_GSR.nc
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as only KS energies are needed for the SKW interpolation:
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To compare the bands with AbiPy, use the *abicomp.py* script:
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To compare the bands with AbiPy, use the |abicomp| script with the `ebands` command:
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```sh
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abicomp.py ebands abinitio_EBANDS.nc skw_EBANDS.nc -p combiplot
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@ -591,8 +593,7 @@ abicomp.py ebands abinitio_EBANDS.nc skw_EBANDS.nc -p combiplot
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If the fit is not satisfactory, you may want to try one of the following options (in order of importance):
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1. Increase the ab-initio mesh in **IBZ_WFK**.
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You may also want to shift the $\kk$-mesh to get closer to the band edge.
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2. Increase the value of `lpratio`
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2. Increase the value of *lpratio*
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3. Play with *rcut* and *rsigma* to damp the oscillations in the interpolant
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Note that it is sometimes difficult to get completely rid of spurious oscillations
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@ -4735,12 +4735,12 @@ subroutine symdm9(ddb, dynmat, gprim, indsym, mpert, natom, nqpt, nsym, rfmeth,&
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index = idir1+ 3*((ipert1-1)+ddb%mpert*((idir2-1)+3*(ipert2-1)))
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!if(ddb%flg(idir1,ipert1,idir2,ipert2,q1)/=1)then
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if(ddb%flg(index,q1)/=1)then
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write(msg, '(a,a,a,i0,a,a,a,4i0,a,a,a,a)' )&
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'Information are missing in the DDB.',ch10,&
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'In block',q1,' the following element is missing :',ch10,&
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'idir1,ipert1,idir2,ipert2=',idir1,ipert1,idir2,ipert2,ch10,&
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'Action: add the required information in the DDB,',ch10,&
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'or modify your input file.'
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write(msg, '(a,a,a,i0,a,a,a,4(i0,1x),a,a,a,a)' )&
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'Elements are missing in the DDB.',ch10,&
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'In block iq1: ',q1,' the following element is missing: ',ch10,&
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'(idir1, ipert1, idir2, ipert2): ',idir1,ipert1,idir2,ipert2,ch10,&
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'Action: add the required information in the DDB with mrgddb,',ch10,&
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'and/or check that all irreducible perturbations have been computed.'
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MSG_ERROR(msg)
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end if
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end do
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