Journal
CHEMISTRY LETTERS
Volume 48, Issue 2, Pages 130-132Publisher
CHEMICAL SOC JAPAN
DOI: 10.1246/cl.180847
Keywords
Solid polymer electrolyte; Lithium-ion battery; Machine learning
Categories
Funding
- 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
Ask authors/readers for more resources
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.
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