Synthesis of lithium-ion Conducting Polymers Designed by Machine Learning-based Prediction and Screening

Kan Hatakeyama, Toshiki Tezuka, Yoshinori Nishikitani, Hiroyuki Nishide, Kenichi Oyaizu

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)130-132
Number of pages3
JournalChemistry Letters
Volume48
Issue number2
DOIs
Publication statusPublished - 2019 Jan 1

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Keywords

  • Lithium-ion battery
  • Machine learning
  • Solid polymer electrolyte

ASJC Scopus subject areas

  • Chemistry(all)

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