Performance Predictors for Organic Cathodes of Lithium-Ion Battery

Kosuke Sakano, Yasuhiko Igarashi, Hiroaki Imai, Shuntaro Miyakawa, Takaya Saito, Yoshiki Takayanagi, Koji Nishiyama, Yuya Oaki

研究成果: Article査読

抄録

Organic cathodes for lithium-ion batteries are one of the most promising and significant materials toward a sustainable society. The molecular design is a key to achieve superior performances beyond inorganic cathodes. The present work shows predictors of the reaction potential, specific capacity, and ideal energy density for organic cathodes. Straightforward prediction models of the performance were constructed by a combination of machine learning and chemical insight, namely, sparse modeling for small data (SpM-S), on a small data set as training data found in the literature. The prediction accuracy was validated using different literature data. The predictors can be applied to explore high-performance organic cathodes in a wide search space efficiently. Moreover, SpM-S afforded straightforward, interpretable, and generalizable prediction models compared to other machine-learning algorithms. The small-data-driven methodology can be applied for further exploration of materials, enhancement of performances, and optimization of processes.

本文言語English
ページ(範囲)2074-2082
ページ数9
ジャーナルACS Applied Energy Materials
5
2
DOI
出版ステータスPublished - 2022 2月 28

ASJC Scopus subject areas

  • 化学工学(その他)
  • エネルギー工学および電力技術
  • 電気化学
  • 電子工学および電気工学
  • 材料化学

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