Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs
<|reference_start|>Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs: 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 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 currently not feasible to image such a high number of particles with full granular detail to achieve representivity. A second challenge is that sufficiently high-resolution 3D imaging techniques remain expensive and are sparsely available at research institutions. To address these challenges, a stereological generative adversarial network (GAN)-based model fitting approach is presented that can generate representative 3D information from 2D data, enabling characterization of materials in 3D using cost-effective 2D data. Once calibrated, this multi-scale model is able to 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.<|reference_end|>
@article{fuchs2024generating,
    title={Generating multi-scale NMC particles with radial grain architectures
  using spatial stochastics and GANs},
    author={Lukas Fuchs, Orkun Furat, Donal P. Finegan, Jeffery Allen, Francois
  L.E. Usseglio-Viretta, Bertan Ozdogru, Peter J. Weddle, Kandler Smith, Volker
  Schmidt},
    journal={arXiv preprint arXiv:2407.05333},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05333},
    primaryClass={physics.app-ph cs.AI}
}