diff --git a/doc/install.rst b/doc/install.rst index 2e184837..0041b8e2 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -12,7 +12,7 @@ Packaged Versions of DFTTools Ubuntu Debian packages ---------------------- -We provide a Debian package for the Ubuntu LTS Versions 16.04 (xenial) and 18.04 (bionic), which can be installed by following the steps outlined :ref:`here `, and the subsequent command:: +We provide a Debian package for the Ubuntu LTS Versions 18.04 (bionic) and 20.04 (focal), which can be installed by following the steps outlined :ref:`here `, and the subsequent command:: sudo apt-get install -y triqs_dft_tools @@ -39,9 +39,9 @@ Compiling DFTTools from source Prerequisites ------------- -#. The :ref:`TRIQS ` library, see :ref:`TRIQS installation instruction `. +#. The :ref:`TRIQS ` library, see :ref:`TRIQS installation instruction `. In the following, we assume that TRIQS is installed in the directory ``path_to_triqs``. -#. Likely, you will also need at least one impurity solver, e.g. the :ref:`CTHYB solver `. +#. Likely, you will also need at least one impurity solver, e.g. the `CTHYB solver `_. Installation steps ------------------ diff --git a/doc/tutorials/images_scripts/NiO_local_lattice_GF.py.rst b/doc/tutorials/images_scripts/NiO_local_lattice_GF.py.rst index f8352c2e..8ee6aa1a 100644 --- a/doc/tutorials/images_scripts/NiO_local_lattice_GF.py.rst +++ b/doc/tutorials/images_scripts/NiO_local_lattice_GF.py.rst @@ -1,7 +1,7 @@ .. _NiO_local_lattice_GF.py: NiO_local_lattice_GF.py ------------ +----------------------- Download :download:`NiO_local_lattice_GF.py <./NiO_local_lattice_GF.py>`. diff --git a/doc/tutorials/svo_elk/srvo3.rst b/doc/tutorials/svo_elk/srvo3.rst index 5509085f..d6b79fcb 100644 --- a/doc/tutorials/svo_elk/srvo3.rst +++ b/doc/tutorials/svo_elk/srvo3.rst @@ -1,6 +1,6 @@ .. _SrVO3_elk: -This example is almost identical to the :ref:`Wien2k-TRIQS SrVO3 example `. On the example of SrVO3 we will discuss now how to set up a full working calculation using Elk, including the initialization of the :ref:`CTHYB solver `. Some additional parameter are introduced to make the calculation more efficient. This is a more advanced example, which is also suited for parallel execution. +This example is almost identical to the :ref:`Wien2k-TRIQS SrVO3 example `. On the example of SrVO3 we will discuss now how to set up a full working calculation using Elk, including the initialization of the :ref:`CTHYB solver `_. Some additional parameter are introduced to make the calculation more efficient. This is a more advanced example, which is also suited for parallel execution. For the convenience of the user, we provide also a full python script (:download:`dft_dmft_cthyb_elk.py `). The user has to adapt it to their own needs. How to execute your script is described :ref:`here`. @@ -48,7 +48,7 @@ First, we load the necessary modules:: import triqs.utility.mpi as mpi The last two lines load the modules for the construction of the -:ref:`CTHYB solver `. +:ref:`CTHYB solver `_. Initializing SumkDFT -------------------- @@ -73,7 +73,7 @@ And next, we can initialize the :class:`SumkDFT ` class:: Initializing the solver ----------------------- -We also have to specify the :ref:`CTHYB solver ` related settings. We assume that the DMFT script for SrVO3 is executed on 16 cores. A sufficient set of parameters for a first guess is:: +We also have to specify the :ref:`CTHYB solver `_ related settings. We assume that the DMFT script for SrVO3 is executed on 16 cores. A sufficient set of parameters for a first guess is:: p = {} # solver @@ -86,7 +86,7 @@ We also have to specify the :ref:`CTHYB solver ` related set p["fit_min_n"] = 30 p["fit_max_n"] = 60 -Here we use a tail fit to deal with numerical noise of higher Matsubara frequencies. For other options and more details on the solver parameters, we refer to :ref:`CTHYB solver ` documentation. It is important to note that the solver parameters have to be adjusted for each material individually. A guide on how to set the tail fit parameters is given :ref:`below `. +Here we use a tail fit to deal with numerical noise of higher Matsubara frequencies. For other options and more details on the solver parameters, we refer to the :ref:`CTHYB solver `_ documentation. It is important to note that the solver parameters have to be adjusted for each material individually. A guide on how to set the tail fit parameters is given :ref:`below `. The next step is to initialize the :class:`solver class `. It consist of two parts: