2018年6月28日
Compositional descriptor-based recommender system for the materials discovery
Journal of Chemical Physics
- ,
- ,
- 巻
- 148
- 号
- 24
- 開始ページ
- 241719
- 終了ページ
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1063/1.5016210
- 出版者・発行元
- American Institute of Physics Inc.
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 crystals can be formed [i.e., chemically relevant compositions (CRCs)]. In addition to data-driven compositional similarity used in the literature, the use of compositional descriptors as a prior knowledge is helpful for the discovery of new compounds. We validate our recommender systems in two ways. First, one database is used to construct a model, while another is used for the validation. Second, we estimate the phase stability for compounds at expected CRCs using density functional theory calculations.
- リンク情報
- ID情報
-
- DOI : 10.1063/1.5016210
- ISSN : 0021-9606
- SCOPUS ID : 85044759699