Combination of recommender system and single-particle diagnosis for accelerated discovery of novel nitrides

Yukinori Koyama, Atsuto Seko, Isao Tanaka, Shiro FUNAHASHI & Naoto HIROSAKI
Discovery of new compounds from wide chemical space is attractive for materials researchers. However, theoretical prediction and validation experiments have not been systematically integrated. Here, we demonstrate that a new combined approach is powerful to accelerate the discovery rate of new compounds significantly, which should be useful for exploration of wide chemical space in general. A recommender system for chemically relevant composition is constructed by machine learning of Inorganic Crystal Structure Database (ICSD) using chemical...
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