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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.05333 |
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| _version_ | 1866914876857778176 |
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| author | Fuchs, Lukas Furat, Orkun Finegan, Donal P. Allen, Jeffery Usseglio-Viretta, Francois L. E. Ozdogru, Bertan Weddle, Peter J. Smith, Kandler Schmidt, Volker |
| author_facet | Fuchs, Lukas Furat, Orkun Finegan, Donal P. Allen, Jeffery Usseglio-Viretta, Francois L. E. Ozdogru, Bertan Weddle, Peter J. Smith, Kandler Schmidt, Volker |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05333 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs Fuchs, Lukas Furat, Orkun Finegan, Donal P. Allen, Jeffery Usseglio-Viretta, Francois L. E. Ozdogru, Bertan Weddle, Peter J. Smith, Kandler Schmidt, Volker Applied Physics Artificial Intelligence 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. |
| title | Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs |
| topic | Applied Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2407.05333 |