@article{eb2e0a3da6e54c56b73f0dc25ae6cb56,
title = "Synthesis of lithium-ion Conducting Polymers Designed by Machine Learning-based Prediction and Screening",
abstract = "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.",
keywords = "Lithium-ion battery, Machine learning, Solid polymer electrolyte",
author = "Kan Hatakeyama-Sato and Toshiki Tezuka and Yoshinori Nishikitani and Hiroyuki Nishide and Kenichi Oyaizu",
note = "Funding Information: This work was partially supported by Grants-in-Aid for Scientific Research (Nos. 17H03072, 18K19120, 18H05515, and 18H05983) from MEXT, Japan. K. H.-S. is grateful for financial support from FS research by JXTG Co. The work was also partially supported by Research Institute for Science and Engineering, Waseda University. We acknowledge Dr. Takeo Suga and Dr. Takahito Nakajima for scientific discussion of polymer synthesis and machine learning. Supporting Information is available on http://dx.doi.org/10.1246/cl.180847. Publisher Copyright: {\textcopyright} 2019 The Chemical Society of Japan",
year = "2019",
doi = "10.1246/cl.180847",
language = "English",
volume = "48",
pages = "130--132",
journal = "Chemistry Letters",
issn = "0366-7022",
publisher = "Chemical Society of Japan",
number = "2",
}
