  Chemically relevant compositions (CRCs) and atomic arrangements of inorganic
compounds have been collected as inorganic crystal structure databases.
Machine-learning is a unique approach to search for currently unknown CRCs from
vast candidates. Herein we propose matrix- and tensor-based recommender system
approaches to predict currently unknown CRCs from database entries of CRCs.
Firstly, the performance of the recommender system approaches to discover
currently unknown CRCs is examined. A Tucker decomposition recommender system
shows the best discovery rate of CRCs as the majority of the top 100
recommended ternary and quaternary compositions correspond to CRCs. Secondly,
systematic density functional theory (DFT) calculations are performed to
investigate the phase stability of the recommended currently unknown CRCs. The
phase stability of the 27 compositions reveals that 23 currently unknown
compounds are stable. These results indicate that the recommender system has
great potential to accelerate the discovery of new compounds.
