Machine-Learning-Driven Advanced Characterization of Battery Electrodes

Donal Finegan, Isaac Squires, Amir Dahari, Steve Kench, Katherine Jungjohann, Samuel Cooper

Research output: Contribution to journalArticlepeer-review

15 Scopus Citations

Abstract

Materials characterization is fundamental to our understanding of lithium ion battery electrodes and their performance limitations. Advances in laboratory-based characterization techniques have yielded powerful insights into the structure-function relationship of electrodes, yet there is still far to go. Further improvements rely, in part, on gaining a deeper understanding of complex physical heterogeneities in the materials. However, practical limitations in characterization techniques inhibit our ability to combine data directly. For example, some characterization techniques are destructive, thus preventing additional analyses on the same region. Fortunately, artificial intelligence (AI) has shown great potential for achieving representative, 3D, multi-modal datasets by leveraging data collected from a range of techniques. In this Perspective, we give an overview of recent advances in lab-based characterization techniques for Li-ion electrodes. We then discuss how AI methods can combine and enhance these techniques, leading to substantial acceleration in our understanding of electrodes.
Original languageAmerican English
Pages (from-to)4368-4378
Number of pages11
JournalACS Energy Letters
Volume7
Issue number12
DOIs
StatePublished - 2022

NREL Publication Number

  • NREL/JA-5700-84720

Keywords

  • artificial intelligence
  • lithium ion battery electrodes
  • materials characterization

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