Search

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 compositional descriptors. Synthesis and identification experiments are made at the chemical compositions with high recommendation scores by the single-particle diagnosis method. Two new compounds, La4Si3AlN9 and La26Si41N80O, and two new variants (isomorphic substitutions) of known compounds, La7Si6N15 and La4Si5N10O, are successfully discovered. Finally, density functional theory calculations are conducted for La4Si3AlN9 to confirm the energetic and dynamical stability and to reveal its atomic arrangement., The Journal of Chemical Physics 154, 224117 (2021)

A derivative structure is a nonequivalent substitutional atomic configuration derived from a given primitive cell. The enumeration of derivative structures plays an essential role in searching for the ground states in multicomponent systems. However, it is computationally hard to enumerate derivative structures if the number of derivative structures of a target system becomes huge. In the present study, we introduce a novel compact data structure of the zero-suppressed binary decision diagram (ZDD) to enumerate derivative structures much more efficiently. The present study shows its simple applications to the enumeration of structures derived from the face-centered cubic and hexagonal close-packed lattices in binary, ternary, and quaternary systems. The present ZDD-based procedure should significantly contribute not only to various computational approaches based on the derivative structures but also to a wide range of combinatorial issues in physics and materials science.

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., Comment: 8 pages, 7 figures