A Capacity-Prediction Model for Exploration of Organic Anodes: Discovery of 5-Formylsalicylic Acid as a High-Performance Anode Active Material

Takumi Komura, Kosuke Sakano, Yasuhiko Igarashi, Hiromichi Numazawa, Hiroaki Imai, Yuya Oaki

Research output: Contribution to journalArticlepeer-review

Abstract

Development of high-performance organic energy storage is one of the important challenges in recent materials science. Molecular design and synthesis have potential for enhancement of the performances. Efficient exploration and design of the molecules are required in a wide search space. In the present work, a capacity prediction model for organic anodes was constructed on small experimental data by sparse modeling, a method of machine learning, combined with our chemical insights. The straightforward linear regression model facilitated discovery of a high-performance active material for organic anodes in a limited number of experiments. A recommended compound, 5-formylsalicylic acid (SA-CHO), showed one of the highest performances in recent works, i.e., a specific capacity of 873 mA h g-1at 100 mA g-1(sample number: n = 3) with rate performance and cycle stability. The model can be applied to explore organic anode active materials with higher specific capacity.

Original languageEnglish
Pages (from-to)8990-8998
Number of pages9
JournalACS Applied Energy Materials
Volume5
Issue number7
DOIs
Publication statusPublished - 2022 Jul 25

Keywords

  • conjugated carbonyls
  • lithium-ion battery
  • machine learning
  • organic anodes
  • predictors
  • sparse modeling

ASJC Scopus subject areas

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

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