Doctoring the docs

Very minor edits

docs/conf.py

@@ -52,8 +52,8 @@
 
 # General information about the project.
 project = "Smact"
-copyright = "2016, Walsh Materials Design Group"
-author = "Walsh Materials Design Group"
+copyright = "2016, Materials Design Group"
+author = "Materials Design Group"
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the

docs/examples.rst

@@ -2,8 +2,8 @@
 Examples
 ========
 
-Here we will give a demonstration of how to use some of `smact`'s features. For a full set of
-work-through examples in Jupyter notebook form check out
+Here we will give a demonstration of how to use some `smact` features. For a full set of
+work-through examples in Jupyter Notebook form check out
 `the examples section of our GitHub repo <https://github.com/WMD-group/SMACT/tree/master/examples>`_.
 For workflows that have been used in real examples and in published work, visit our
 `separate repository <https://github.com/WMD-group/smact_workflows>`_.
@@ -12,7 +12,7 @@ For workflows that have been used in real examples and in published work, visit
 Element and species classes
 ===========================
 
-The element and species classes are at the heart of :mod:`smact`'s functionality. Elements are the
+The element and species classes are at the heart of :mod:`smact` functionality. Elements are the
 elements of the periodic table. Species are elements, with some additional information; the
 oxidation state and the coordination environment (if known). So for example the element iron
 can have many oxidation states and those oxidation states can have many coordination
@@ -29,7 +29,7 @@ environments.
     The element Fe has 8 oxidation states. They are [-2, -1, 1, 2, 3, 4, 5, 6].
 
 When an element has an oxidation state and coordination environment then it has additional
-features. For example the Shannon radius [1]_ of the element, this is often useful for calculating
+features. For example, the Shannon radius [1]_ of the element is useful for calculating
 radius ratio rules [2]_, or for training neural networks [3]_ .
 
 .. code:: python
@@ -43,7 +43,7 @@ radius ratio rules [2]_, or for training neural networks [3]_ .
 List building
 =============
 
-Often when using :mod:`smact` the aim will to be to search over combinations of a set of elements. This
+Often when using :mod:`smact` the aim will be to search over combinations of a set of elements. This
 is most efficiently achieved by setting up a dictionary of the elements that you want to search
 over. The easiest way to achieve this in :mod:`smact` is to first create a list of the symbols of the elements
 that you want to include, then to build a dictionary of the corresponding element objects.
@@ -88,7 +88,7 @@ the search.
 Neutral combinations
 ====================
 
-One of the most basic tests for establishing sensible combinations of elements is that they should form charge neutral
+One of the most basic tests for establishing sensible combinations of elements is that they should form charge-neutral
 combinations. This is a straightforward combinatorial problem of comparing oxidation states and allowed stoichiometries.
 
 :math:`\Sigma_i Q_in_i = 0`
@@ -211,32 +211,30 @@ in function to calculate this property for a given composition.
 Interfacing to machine learning
 ===============================
 
-When preparing to do machine learning, we have to convert the compositions that we have into
+When preparing to build machine learning models, we have to convert the chemical compositions into
 something that can be fed into an algorithm. Many of the properties provided in :mod:`smact` are suitable for this,
-one can take properties like electronegativity, mass, electron affinity etc etc (for the full list see
+one can take properties like electronegativity, mass, electron affinity, etc. (for the full list see
 :ref:`smact_module`).
 
-One useful representation that is often used in machine learning is the one-hot-vector formulation. A similar
-construction to this can be used to encode a chemical formula. A vector of length of the periodic table is
-set up and each element set to be a number corresponding to the stoichiometric ratio of that element in the compound.
+One useful representation in machine learning is the one-hot-vector formulation. A similar
+construction to this can be used to encode a chemical formula. A vector of length covering the periodic table is
+constructed and each element is set to a number corresponding to the stoichiometric ratio of that element in the compound.
 For example we could convert :math:`Ba(OH)_2`
 
 .. code:: python
 
    ml_vector = smact.screening.ml_rep_generator(['Ba', 'H', 'O'], stoichs=[1, 2, 2])
 
 There is also `an example <https://github.com/WMD-group/SMACT/blob/master/examples/Counting/Generate_compositions_lists.ipynb>`_
-demonstrating the conversion of charge neutral compositions produced by `smact` to a list of formulas using Pymatgen,
+demonstrating the conversion of charge-neutral compositions produced by `smact` to a list of formulas using Pymatgen,
 or to a Pandas dataframe, both of which could then be used as input for a machine learning algorithm.
 For a full machine learning example that uses `smact`, there is a repository `here <https://github.com/WMD-group/Solar_oxides_data>`_ 
 which demonstrates a search for solar energy materials from the four-component (quaternary) oxide materials space.
 
-.. [1]  "Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides".
-         Acta Crystallogr A. 32: 751–767, 1976
+.. [1]  "Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides" Acta Cryst. A. **32**, 751–767 (1976).
 
-.. [2]  "Crystal Structure and Chemical Constitution" Trans. Faraday Soc. 25, 253-283, 1929.
+.. [2]  "Crystal structure and chemical constitution" Trans. Faraday Soc. **25**, 253-283 (1929).
 
-.. [3] "Deep neural networks for accurate predictions of crystal stability" Nat. Comms. 9, 3800, 2018.
+.. [3] "Deep neural networks for accurate predictions of crystal stability" Nat. Comms. **9**, 3800 (2018).
 
-.. [4] "Prediction of Flatband Potentials at Semiconductor‐Electrolyte Interfaces from Atomic Electronegativities"
-       J. Electrochem. Soc. 125, 228-32, 1975.
+.. [4] "Prediction of flatband potentials at semiconductor‐electrolyte interfaces from atomic electronegativities" J. Electrochem. Soc. **125**, 228-232 (1975).

docs/getting_started.rst

@@ -15,7 +15,7 @@ and `pymatgen <http://pymatgen.org>`_ are also required for many components.
 Installation
 ============
 
-The latest stable release of SMACT can be installed via pip which will automatically setup other Python packages as required:
+The latest stable release of SMACT can be installed via pip, which will automatically setup other Python packages as required:
 
 .. code::
 

docs/introduction.rst

@@ -3,25 +3,25 @@ Introduction
 ============
 
 :mod:`smact` is a collection of tools and examples for "low-fi" screening of
-potential semiconducting materials through the use of simple chemical
+potential semiconducting materials through the use of chemical
 rules.
 
 :mod:`smact` uses a combination of heuristics and models derived from data to
 rapidly search large areas of chemical space. This combination of methods
 allows :mod:`smact` to identify new materials for applications such as photovoltaics,
 water splitting and thermoelectrics. Read more about :mod:`smact` in our publications:
 
-- `Computational Screening of All Stoichiometric Inorganic Materials <https://www.sciencedirect.com/science/article/pii/S2451929416301553>`_
+- `Computational screening of all stoichiometric inorganic materials <https://www.sciencedirect.com/science/article/pii/S2451929416301553>`_
 - `Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure <http://pubs.rsc.org/en/content/articlehtml/2017/sc/c7sc03961a>`_
 - `Materials discovery by chemical analogy: role of oxidation states in structure prediction <http://pubs.rsc.org/en/content/articlehtml/2018/fd/c8fd00032h>`_
 
-This approach is heavily inspired by the work of Harrison [1]_ and
-Pamplin [2]_. The work is an active project in the `Walsh Materials Design Group <http://wmd-group.github.io>`_.
+This approach is inspired by the work of Harrison [1]_ and
+Pamplin [2]_. The work is an active project in the `Materials Design Group <http://wmd-group.github.io>`_.
 
-SMACT is now available *via* :code:`pip install smact`.
+The package is available *via* :code:`pip install smact`.
 
-We are also developing a set of Jupyter notebook examples `here <https://github.com/WMD-group/SMACT/tree/master/examples>`_.
+We are also developing a set of Jupyter Notebook examples `here <https://github.com/WMD-group/SMACT/tree/master/examples>`_.
 
 .. [1] http://www.worldcat.org/oclc/5170450 Harrison, W. A. *Electronic structure and the properties of solids: the physics of the chemical bond* (1980)
 
-.. [2] http://dx.doi.org/10.1016/0022-3697(64)90176-3 Pamplin, B. R. *J. Phys. Chem. Solids* (1964) **7** 675--684
+.. [2] http://dx.doi.org/10.1016/0022-3697(64)90176-3 Pamplin, B. R. *A systematic method of deriving new semiconducting compounds by structural analogy* J. Phys. Chem. Solids **7**, 675--684 (1964)

docs/smact.builder.rst

@@ -2,7 +2,7 @@ smact.builder module
 ====================
 
 A collection of functions for building certain lattice types.
-Currently there are examples here for the Perovskite and Wurzite lattice types,
+Currently, there are examples here for the perovskite and wurtzite structure types,
 which rely on the Atomic Simulation Environment (ASE)
 :func:`spacegroup.crystal` function.
 

docs/smact.dopant_prediction.doper.rst

@@ -1,7 +1,8 @@
 smact.dopant_prediction.doper module
 =============================
 
-A class to create possible n-type and p-type dopants
+A class to create possible n-type and p-type dopants according to 
+their accessible oxidation states.
 
 .. automodule:: smact.dopant_prediction.doper
     :members:

docs/smact.properties.rst

@@ -1,7 +1,8 @@
 smact.properties module
 =======================
 
-A collection of tools for estimating useful properties.
+A collection of tools for estimating physical properties
+based on chemical composition.
 
 The "electronegativity of a compound" computed with
 :func:`compound_electroneg` is the rescaled geometric mean of
@@ -12,15 +13,15 @@ photoelectric threshold: [1]_
 
 In other words, the computed group
 :math:`2.86(\chi_{A}\chi_{B})^{1/2}`
-is the mid-gap energy and the VBM/CBM positions can be estimated by
-subtracting/adding half of the band gap :math:`E_g`.
+is the mid-gap energy. The valence band maximum/conduction band minimum positions 
+can be estimated by subtracting/adding half of the band gap :math:`E_g`.
 This is an extension Mulliken's electronegativity scale in which
 :math:`\chi_{A} = (I_{A} + E_{A})/2` (where :math:`I` and :math:`E`
 are respectively the ionisation potential and electron affinity.) [2]_
 
-.. [1] Nethercot, A. H. (1974). *Phys. Rev. Lett.*, **33**, 1088–1091. http://dx.doi.org/10.1103/PhysRevLett.33.1088
+.. [1] Nethercot, A. H., *Prediction of Fermi energies and photoelectric thresholds based on electronegativity concepts* Phys. Rev. Lett. **33**, 1088–1091 (1974). http://dx.doi.org/10.1103/PhysRevLett.33.1088
 
-.. [2] Mulliken, R. S. (1934). *J. Chem. Phys.*, **2**, 782. http://dx.doi.org/10.1063/1.1749394
+.. [2] Mulliken, R. S., *A new electroaffinity scale; together with data on valence states and on valence ionization potentials and electron affinities* J. Chem. Phys. **2**, 782 (1934). http://dx.doi.org/10.1063/1.1749394
 
 .. automodule:: smact.properties
     :members:

docs/smact.rst

@@ -11,7 +11,7 @@ which returns a dictionary of :class:`smact.Element` objects indexed
 by their chemical symbols.
 Generating this dictionary once and then performing lookups is
 generally the fastest way of accessing element data while enumerating
-possibilities.
+possibilities in chemical space.
 
 .. automodule:: smact
     :members:

docs/smact.structure_prediction.probability_models.rst

@@ -2,7 +2,7 @@ Substitution Probability Models
 ===============================
 
 Minimal API for developing substitution likelihood probability models
-for ion mutation.
+for species mutation.
 
 .. automodule:: smact.structure_prediction.probability_models
     :members: