Generating Multi-Scale Li-Ion Battery Cathode Particles with Radial Grain Architectures Using Stereological Generative Adversarial Networks: Article No. 4

Lukas Fuchs, Orkun Furat, Donal Finegan, Jeffery Allen, Francois Usseglio-Viretta, Bertan Ozdogru, Peter Weddle, Kandler Smith, Volker Schmidt

Research output: Contribution to journalArticlepeer-review

Abstract

Understanding structure-property relationships of Li-ion battery cathodes is crucial for optimizing rate-performance and cycle-life resilience. However, correlating the morphology of cathode particles, such as in LiNi0.8Mn0.1Co0.1O2 (NMC811), and their inner grain architecture with electrode performance is challenging, particularly, due to the significant length-scale difference between grain and particle sizes. Experimentally, it is not feasible to image such a high number of particles with full granular detail. A second challenge is that sufficiently high-resolution 3D imaging techniques remain expensive and are sparsely available at research institutions. Here, we present a stereological generative adversarial network-based model fitting approach to tackle this, that generates representative 3D information from 2D data, enabling characterization of materials in 3D using cost-effective 2D data. Once calibrated, this multi-scale model can rapidly generate virtual cathode particles that are statistically similar to experimental data, and thus is suitable for virtual characterization and materials testing through numerical simulations. A large dataset of simulated particles with inner grain architecture has been made publicly available.
Original languageAmerican English
Number of pages13
JournalCommunications Materials
Volume6
DOIs
StatePublished - 2025

NREL Publication Number

  • NREL/JA-5700-91478

Keywords

  • GAN
  • grain architecture
  • multi-scale model
  • NMC811
  • stereology
  • tessellation
  • virtual cathode particle

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