Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs

• URL: http://arxiv.org/abs/2407.05333v2
• Date: Fri, 19 Jul 2024 08:44:39 GMT
• Title: Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs
• Authors: Lukas Fuchs, Orkun Furat, Donal P. Finegan, Jeffery Allen, Francois L. E. Usseglio-Viretta, Bertan Ozdogru, Peter J. Weddle, Kandler Smith, Volker Schmidt,
• Abstract summary: correlating morphology of cathode particles with electrode performance is challenging. It is currently not feasible to image such a high number of particles with full granular detail to achieve representivity. A stereological generative adversarial network (GAN)-based model fitting approach is presented that can generate representative 3D information from 2D data. Once calibrated, this multi-scale model is able to rapidly generate virtual cathode particles that are statistically similar to experimental data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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