  Structures and properties of many inorganic compounds have been collected
historically. However, it only covers a very small portion of possible
inorganic crystals, which implies the presence of numerous currently unknown
compounds. A powerful machine-learning strategy is mandatory to discover new
inorganic compounds from all chemical combinations. Herein we propose a
descriptor-based recommender-system approach to estimate the relevance of
chemical compositions where stable crystals can be formed [i.e., chemically
relevant compositions (CRCs)]. As well as data-driven compositional similarity
used in the literature, the use of compositional descriptors as a prior
knowledge can accelerate the discovery of new compounds. We validate our
recommender systems in two ways. Firstly, one database is used to construct a
model, while another is used for the validation. Secondly, we estimate the
phase stability for compounds at expected CRCs using density functional theory
calculations.
