期刊
COMPUTATIONAL MATERIALS SCIENCE
卷 112, 期 -, 页码 364-367出版社
ELSEVIER
DOI: 10.1016/j.commatsci.2015.11.013
关键词
Material informatics; Density functional theory; Machine learning; Material big data
资金
- Japan Society for the Promotion of Science
- Grants-in-Aid for Scientific Research [14J00040] Funding Source: KAKEN
Desired material synthesis and design can be directly predicted on the basis of first principle calculations and machine learning. Material big data is constructed based on density functional theory where every possible element combinations are considered and then used as training sets for support vector machines. The predicted material properties for common materials are successfully matched with experimental data. In addition, material combinations based on desired material properties are also able to be predicted. Thus, the proposed work flow becomes the bridge between the material database and designing materials. The approach enables efficient material mining from material big data and could potentially reveal undiscovered desired materials. This approach could also potentially enable targeted material mining from material big data, the unveiling of undiscovered desired materials, and the execution of targeted material synthesis in experiment. (C) 2015 Elsevier B.V. All rights reserved.
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