- added Applications and Real-World Examples to the docs index page

docs/index.rst

@@ -46,15 +46,16 @@ is given in
 
 - Adam M. Krajewski, Jonathan W. Siegel, Jinchao Xu, Zi-Kui Liu, Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks, Computational Materials Science, Volume 208, 2022, 111254 `(https://doi.org/10.1016/j.commatsci.2022.111254) <https://doi.org/10.1016/j.commatsci.2022.111254>`_
 
-**News:**
+News
+----
 
 -  **(v0.11.0)** Some common questions are now addressed in the
    `documentation FAQ
    section <https://pysipfenn.readthedocs.io/en/stable/faq.html>`__.
 -  **(v0.11.0)** The model downloads from Zenodo are now multithreaded
    and are 15 times faster.
 -  **(March 2023 Workshop)** We would like to thank all of our amazing
-   attendees for making our workshop, co-orgazined with the `Materials
+   attendees for making our workshop, co-organized with the `Materials
    Genome Foundation <https://materialsgenomefoundation.org>`__, such a
    success! Over 100 of you simultaneously followed all exercises and,
    at the peak, we loaded over 1,200GB of models into the HPC’s RAM. At
@@ -67,6 +68,9 @@ is given in
 .. note::
    This project is under active development. We recommend using released (stable) versions.
 
+Index
+-----
+
 .. toctree::
    install
    faq
@@ -77,3 +81,43 @@ is given in
    genindex
    :maxdepth: 2
    :caption: Contents
+
+Applications
+------------
+
+pySIPFENN is a very flexible tool that can, in principle, be used for
+the prediction of any property of interest that depends on an atomic
+configuration with very few modifications. The models shipped by
+default are trained to predict formation energy because that is what our
+research group is interested in; however, if one wanted to predict
+Poisson’s ratio and trained a model based on the same features, adding
+it would take minutes. Simply add the model in open ONNX format and link
+it using the *models.json* file, as described in the documentation.
+
+Real-World Examples
+-------------------
+
+In our line of work, pySIPFENN and the formation energies it predicts are
+usually used as a computational engine that generates proto-data for
+creation of thermodynamic databases (TDBs) using ESPEI
+(https://espei.org). The TDBs are then used through pycalphad
+(https://pycalphad.org) to predict phase diagrams and other
+thermodynamic properties.
+
+Another of its uses in our research is guiding the Density of Functional
+Theory (DFT) calculations as a low-cost screening tool. Their efficient
+conjunction then drives experiments leading to the discovery of new
+materials, as presented in these two papers:
+
+-  Sanghyeok Im, Shun-Li Shang, Nathan D. Smith, Adam M. Krajewski,
+   Timothy Lichtenstein, Hui Sun, Brandon J. Bocklund, Zi-Kui Liu,
+   Hojong Kim, Thermodynamic properties of the Nd-Bi system via emf
+   measurements, DFT calculations, machine learning, and CALPHAD
+   modeling, Acta Materialia, Volume 223, 2022, 117448,
+   https://doi.org/10.1016/j.actamat.2021.117448.
+
+-  Shun-Li Shang, Hui Sun, Bo Pan, Yi Wang, Adam M. Krajewski, Mihaela
+   Banu, Jingjing Li & Zi-Kui Liu, Forming mechanism of equilibrium and
+   non-equilibrium metallurgical phases in dissimilar aluminum/steel
+   (Al–Fe) joints. Nature Scientific Reports 11, 24251 (2021).
+   https://doi.org/10.1038/s41598-021-03578-0