Unified graph building and featurizing for Weave.jl, AtomicGraphNets.jl, and (maybe soon) more!

Documentation is starting to be built in the wiki!

• Build graphs (as SimpleWeightedGraphs) from CIF files using PyCall to pymatgen functions
• Visualization using GraphPlot, check out the visualize_graph function in the graph_functions.jl file, you can make pretty pictures like these, whether the graph is simpler or more complicated (thickness of connections indicates weight of edge in graph (higher weights for nearer neighbors)):

(NB: this animation's syntax is slightly out of date, new one to come!)

• Flexible featurization (currently onehot-style) and decoding: choose features to include, level of discretization, etc., and directly decode feature vectors to check values:

julia> features = Symbol.(["Group", "Row", "Block", "Atomic mass", "Atomic radius", "X"])
6-element Array{Symbol,1}:
 :Group
 :Row
 :Block
 Symbol("Atomic mass")
 Symbol("Atomic radius")
 :X

julia> atom_feature_vecs, featurization = make_feature_vectors(features)
[ Info: 16 elements were dropped so that all features are defined.

julia> decode_feature_vector(atom_feature_vecs["Si"], featurization)
Dict{Symbol,Any} with 6 entries:
  Symbol("Atomic mass")   => (27.1071, 53.2064)
  Symbol("Atomic radius") => (0.955, 1.19)
  :Group                  => 14
  :Row                    => 3
  :Block                  => "p"
  :X                      => (1.684, 2.012)

Sean to add...

• Julia 1.4+
• packages listed in Project.toml
• In addition, you will need your PyCall to have access to the pymatgen package, which can be added using Conda.jl as: Conda.add("pymatgen"; channel="conda-forge"), as well as the rdkit package (Conda.add("rdkit"; channel="conda-forge"))

• "hybrid" featurizations using features from multiple paradigms if available
• more input file formats? e.g. SELFIES