  With the availability of extensive databases of inorganic materials,
data-driven approaches leveraging machine learning have gained prominence in
materials science research. In this study, we propose an innovative adaptation
of data-driven concepts to the mapping and exploration of chemical compound
space. Recommender systems, widely utilized for suggesting items to users,
employ techniques such as collaborative filtering, which rely on bipartite
graphs composed of users, items, and their interactions. Building upon the Open
Quantum Materials Database (OQMD), we constructed a bipartite graph where
elements from the periodic table and sites within crystal structures are
treated as separate entities. The relationships between them, defined by the
presence of ions at specific sites and weighted according to the thermodynamic
stability of the respective compounds, allowed us to generate an embedding
space that contains vector representations for each ion and each site. Through
the correlation of ion-site occupancy with their respective distances within
the embedding space, we explored new ion-site occupancies, facilitating the
discovery of novel stable compounds. Moreover, the graph's embedding space
enabled a comprehensive examination of chemical similarities among elements,
and a detailed analysis of local geometries of sites. To demonstrate the
effectiveness and robustness of our method, we conducted a historical
evaluation using different versions of the OQMD and recommended new compounds
with Kagome lattices, showcasing the applicability of our approach to practical
materials design.
