期刊
CHEMISTRY LETTERS
卷 48, 期 2, 页码 130-132出版社
CHEMICAL SOC JAPAN
DOI: 10.1246/cl.180847
关键词
Solid polymer electrolyte; Lithium-ion battery; Machine learning
资金
- MEXT, Japan [17H03072, 18K19120, 18H05515, 18H05983]
- FS research by JXTG Co.
- Research Institute for Science and Engineering, Waseda University
- Grants-in-Aid for Scientific Research [18H05983, 18K19120, 17H03072, 18H05515] Funding Source: KAKEN
A database for 240 types of lithium-ion conducting solid polymer electrolytes was newly constructed and analyzed by machine learning. Despite the complexity of the polymer composites as electrolytes, accurate prediction was achieved by the appropriate learning model. Inspired by the analyses, poly(glycidyl ether) derivatives were synthesized to yield higher conductivity. Screening of single-ion conducting polymers with de novo design (>15000 candidates) was also conducted based on the established database.
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