Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 148, Issue 24, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.5016210
Keywords
-
Funding
- PRESTO, JST
- Japan Society for the Promotion of Science (JSPS) [25106005]
- Materials research by Information Integration Initiative (MI2I) from JST
- JSPS [15H02286]
- Grants-in-Aid for Scientific Research [15H02286] Funding Source: KAKEN
Ask authors/readers for more resources
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. Published by AIP Publishing.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available