Update index.rst

Add citation section and minor grammatical checks

docs/source/index.rst

@@ -15,38 +15,48 @@ Overview
 --------
 **XenonPy** is a Python library that implements a comprehensive set of machine learning tools
 for materials informatics. Its functionalities partially depend on Python (PyTorch) and R (MXNet).
-This package still under hard working. The current release provides some limited features:
+This package is still under development. The current released version provides some limited features:
 
 * Interface to the public materials database
 * Library of materials descriptors (compositional/structural descriptors)
-* pre-trained model library **XenonPy.MDL** (v0.1.0.beta, 2019/8/9: more than 140,000 models (include private models) in 35 properties of small molecules, polymers, and inorganic compounds) [Currently under maintenance, expected to be recovered in v0.7]
+* pre-trained model library **XenonPy.MDL** (v0.1.0.beta, 2019/8/9: more than 140,000 models (include private models) in 35 properties of small molecules, polymers, and inorganic compounds) [Currently under major maintenance, expected to be recovered in v0.7]
 * Machine learning tools.
 * Transfer learning using the pre-trained models in XenonPy.MDL
 
 
 .. image:: _static/xenonpy.png
 
 
+------
+Citation
+------
+XenonPy is an on-going research project that covers multiple important topics in materials informatics.
+We recommend users to cite the papers that are relevant to their specific use of XenonPy.
+Please refer to :doc:`features` for details of each feature in XenonPy with its corresponding citation.
+User can also check the publication list below to pick the relevant citations.
+
+
 --------
 Features
 --------
 XenonPy has a rich set of tools for various materials informatics applications.
 The descriptor generator class can calculate several types of numeric descriptors from ``compositional``, ``structure``.
 By using XenonPy's built-in visualization functions, the relationships between descriptors and target properties can be easily shown in a heatmap.
+XenonPy also supports an interface to use the ``rdkit`` descriptors and provides the ``iQSPR`` algorithm for molecular design.
 
 Transfer learning is an important tool for the efficient application of machine learning methods to materials informatics.
 To facilitate the widespread use of transfer learning,
 we have developed a comprehensive library of pre-trained models, called **XenonPy.MDL**.
 This library provides a simple API that allows users to fetch the models via an HTTP request.
 For the ease of using the pre-trained models, some useful functions are also provided.
 
-See :doc:`features` for details
+See :doc:`features` for details.
 
 
 ------
 Sample
 ------
-Some samples are available here: https://github.com/yoshida-lab/XenonPy/tree/master/samples
+Sample codes of different features in XenonPy are available here: https://github.com/yoshida-lab/XenonPy/tree/master/samples
 
 
 .. _user-doc:
@@ -82,7 +92,7 @@ Contributing
 
 XenonPy is an `open source project <https://github.com/yoshida-lab/XenonPy>`_ inspired by `matminer <https://hackingmaterials.github.io/matminer>`_.
 :raw-html:`<br/>`
-This project is under on-going development. We would appreciate any feedback from the users.
+This project is under continuous development. We would appreciate any feedback from the users.
 :raw-html:`<br/>`
 Code contributions are also very welcomed. See :doc:`contribution` for more details.