Recommender system for discovery of inorganic compounds

A recommender system based on experimental databases is useful for the efficient discovery of inorganic compounds. Here, we review studies on the discovery of as-yet-unknown compounds using recommender systems. The first method used compositional descriptors made up of elemental features. Chemical compositions registered in the inorganic crystal structure database (ICSD) were supplied to machine learning for binary classification. The other method did not use any descriptors, but a tensor decomposition technique was adopted. The predictive performance for currently unknown chemically relevant compositions (CRCs) was determined by examining their presence in other databases. According to the recommendation, synthesis experiments of two pseudo-ternary compounds with currently unknown structures were successful. Finally, a synthesis-condition recommender system was constructed by machine learning of a parallel experimental data-set collected in-house using a polymerized complex method. Recommendation scores for unexperimented conditions were then evaluated. Synthesis experiments under the targeted conditions found two yet-unknown pseudo-binary oxides.

Automated summary

A recommender system based on experimental databases is crucial for the discovery of inorganic compounds. This review examines different methods used in the discovery of unknown compounds and demonstrates their effectiveness. By using compositional descriptors or tensor decomposition techniques, predictive performance can be improved, leading to successful synthesis of previously unknown compounds.