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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)2074-2082
Number of pages9
JournalACS Applied Energy Materials
Volume5
Issue number2
DOIs
Publication statusPublished - 2022 Feb 28

Keywords

  • lithium-ion battery
  • machine learning
  • organic cathodes
  • prediction models
  • quinone derivatives
  • sparse modeling

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Electrochemistry
  • Electrical and Electronic Engineering
  • Materials Chemistry

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