Commit 94f10612 authored by Marco Govoni's avatar Marco Govoni
Browse files

Updated documentation

parent 5d73d7de
......@@ -17,8 +17,10 @@ Developers
- Sijia Dong (Argonne National Laboratory)
- Marco Govoni (Argonne National Laboratory and University of Chicago)
- He Ma (University of Chicago)
- Yu Jin (University of Chicago)
- Nan Sheng (University of Chicago)
- Han Yang (University of Chicago)
- Wenzhe Yu (Argonne National Laboratory)
Contributors
------------
......@@ -35,7 +37,10 @@ Former Developers
-----------------
- Nicholas Brawand (2016-2018)
- Sijia Dong (2019-2020)
- Matteo Gerosa (2017-2018)
- Lan Huang (2019-2020)
- He Ma (2017-2020)
- Ryan McAvoy (2017-2018)
- Ngoc Linh Nguyen (2017-2018)
- Peter Scherpelz (2016-2018)
......
......@@ -60,7 +60,7 @@ master_doc = 'index'
# General information about the project.
project = data["name"]
copyright = u'2020, Marco Govoni'
copyright = u'2021, Marco Govoni'
author = u'Marco Govoni'
# The version info for the project you're documenting, acts as replacement for
......@@ -311,6 +311,5 @@ texinfo_documents = [
#texinfo_no_detailmenu = False
intersphinx_mapping = {
'python': ('https://docs.python.org/3.6', None),
'pymongo': ('https://api.mongodb.com/python/current/', None)
'python': ('https://docs.python.org/3.6', None)
}
......@@ -4,7 +4,9 @@
Installation
============
In order to install WEST you need to download the `QuantumEspresso 6.1 <https://gitlab.com/QEF/q-e/-/archive/qe-6.1.0/q-e-qe-6.1.0.tar>`_.
In order to install WEST you need to download the `QuantumEspresso 6.1.0 <https://gitlab.com/QEF/q-e/-/archive/qe-6.1.0/q-e-qe-6.1.0.tar>`_.
To compute absorption spectra (BSE), you also need to download and install `Qbox <http://qboxcode.org>`_.
`QuantumEspresso <http://www.quantum-espresso.org/>`_ (QE) is an integrated suite of Open-Source computer codes for electronic-structure calculations and materials modeling at the nanoscale, based on density-functional theory (DFT), plane waves (PW), and pseudopotentials (PP).
Configure QuantumEspresso by running the ``configure`` script that comes with the QE distribution. WEST requires `MPI <https://en.wikipedia.org/?title=Message_Passing_Interface>`_ support (`ScaLAPACK <http://www.netlib.org/scalapack/>`_ and `OpenMP <http://openmp.org/>`_ support is also recommended, but optional). If all the environment variables (compilers, libraries etc.) have been set according to the QE configure guide, this would simply be:
......
......@@ -531,7 +531,7 @@ wfreq_control
* - **Default**
- 204
* - **Description**
- Number of frequecies used to plot the spectral function (runlevel "P"), sampling the interval [-ecut_spectralf[0],ecut_spectralf[1]].
- Number of frequecies used to plot the spectral function (runlevel "P"), sampling the interval [ecut_spectralf[0],ecut_spectralf[1]].
|
......
......@@ -5,11 +5,13 @@ Overview
**WEST** (Without Empty STates) is a massively parallel software for large scale electronic structure calculations within many-body perturbation theory.
Features:
Features:
- GW (full-frequency)
- BSE *under development*
- electron-phonon *under development*
- GW self-energy (full-frequency)
- Absorption spectra (BSE) in *beta-release*
- Electron-phonon *under development*
- Quantum embedding (QDET) *under development*
- GPU-porting *under development*
.. seealso::
**WESTpy** is a Python package, designed to assist users of the WEST code in pre- and post-process massively parallel calculations. Click `here <http://www.west-code.org/doc/westpy/latest/>`_ to know more.
{
"cells": [
 
 
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial can be downloaded [link](http://greatfire.uchicago.edu/west-public/West/raw/master/Doc/tutorials/west_100.ipynb)."
]
......@@ -11,10 +11,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.0 Getting Started: GW calculation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to compute the GW electronic structure of the silane molecule you need to run `pw.x`, `wstat.x` and `wfreq.x` in sequence. Documentation for building and installing WEST is available at this [link](http://www.west-code.org/doc/West/latest/installation.html)."
......
%% Cell type:markdown id: tags:
This tutorial can be downloaded [link](http://greatfire.uchicago.edu/west-public/West/raw/master/doc/tutorials/west_200.ipynb).
This tutorial can be downloaded [link](http://greatfire.uchicago.edu/west-public/West/raw/master/Doc/tutorials/west_200.ipynb).
%% Cell type:markdown id: tags:
# 2.0 Analyzing the frequency dependency in GW calculations
%% Cell type:markdown id: tags:
We analyze the frequency dependent self-energy and understand the difference between the solution of the Quasiparticle equation with or without first-order expansion of the self-energy in the frequency domain.
In particular we want to compare the following solutions to the quasiparticle equation:
We analyze the frequency dependency of the GW self-energy, by exploring two ways of solving the Quasiparticle equation:
- without linearization
\begin{align}
E-\varepsilon_{KS} = \langle \Sigma(E) -V_{xc} \rangle
......@@ -26,11 +24,11 @@
where $\varepsilon_{KS}$ is the Kohn-Sham energy, and $E$ is the QP energy obtained at the $G_0W_0$ level of theory.
%% Cell type:markdown id: tags:
After doing step 1 and step 2 of the [1.0 Tutorial](http://greatfire.uchicago.edu/west-public/West/raw/master/doc/tutorials/west_200.ipynb), we download the following file:
After completing step 1 and step 2 of the [1.0 Tutorial](http://greatfire.uchicago.edu/west-public/West/raw/master/doc/tutorials/west_200.ipynb), we download the following file:
%% Cell type:code id: tags:
``` python
%%bash
......@@ -61,11 +59,11 @@
%% Cell type:markdown id: tags:
The output file ``wfreq.out`` contains information about the calculation of the GW self-energy, and the corrected electronic structure can be found in the file ``<west_prefix>.wfreq.save/wfreq.json``.
Below we show how to load, print, and plot the frequency-dependent quasiparticle corrections.
Below we show how to load and plot the frequency-dependent quasiparticle corrections.
%% Cell type:code id: tags:
``` python
import json
......@@ -141,10 +139,14 @@
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
We see that `eqpLin` (green dotted line) and `eqpSec` (blue dotted line) correspond to the solution of the Quasiparticle equation with and without linearization of the frequency dependency, respectively.
%% Cell type:code id: tags:
``` python
```
......
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