  We define bipartite and monopartite relational networks of chemical elements
and compounds using two different datasets of inorganic chemical and material
compounds, as well as study their topology. We discover that the connectivity
between elements and compounds is distributed exponentially for materials, and
with a fat tail for chemicals. Compounds networks show similar distribution of
degrees, and feature a highly-connected club due to oxygen. Chemical compounds
networks appear more modular than material ones, while the communities detected
reveal different dominant elements specific to the topology. We successfully
reproduce the connectivity of the empirical chemicals and materials networks by
using a family of fitness models, where the fitness values are derived from the
abundances of the elements in the aggregate compound data. Our results pave the
way towards a relational network-based understanding of the inherent complexity
of the vast chemical knowledge atlas, and our methodology can be applied to
other systems with the ingredient-composite structure.
