Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks
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 electrod...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Materials |
| Online Access: | https://doi.org/10.1038/s43246-024-00728-5 |
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| author | Lukas Fuchs Orkun Furat Donal P. Finegan Jeffery Allen Francois L. E. Usseglio-Viretta Bertan Ozdogru Peter J. Weddle Kandler Smith Volker Schmidt |
| author_facet | Lukas Fuchs Orkun Furat Donal P. Finegan Jeffery Allen Francois L. E. Usseglio-Viretta Bertan Ozdogru Peter J. Weddle Kandler Smith Volker Schmidt |
| author_sort | Lukas Fuchs |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3e11cf138cff4b59a1425b34056c4c42 |
| institution | Kabale University |
| issn | 2662-4443 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Materials |
| spelling | doaj-art-3e11cf138cff4b59a1425b34056c4c422025-01-12T12:32:49ZengNature PortfolioCommunications Materials2662-44432025-01-016111310.1038/s43246-024-00728-5Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networksLukas Fuchs0Orkun Furat1Donal P. Finegan2Jeffery Allen3Francois L. E. Usseglio-Viretta4Bertan Ozdogru5Peter J. Weddle6Kandler Smith7Volker Schmidt8Ulm University, Institute of StochasticsUlm University, Institute of StochasticsNational Renewable Energy LaboratoryNational Renewable Energy LaboratoryNational Renewable Energy LaboratoryNational Renewable Energy LaboratoryNational Renewable Energy LaboratoryNational Renewable Energy LaboratoryUlm University, Institute of StochasticsAbstract 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.https://doi.org/10.1038/s43246-024-00728-5 |
| spellingShingle | Lukas Fuchs Orkun Furat Donal P. Finegan Jeffery Allen Francois L. E. Usseglio-Viretta Bertan Ozdogru Peter J. Weddle Kandler Smith Volker Schmidt Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks Communications Materials |
| title | Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks |
| title_full | Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks |
| title_fullStr | Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks |
| title_full_unstemmed | Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks |
| title_short | Generating multi-scale Li-ion battery cathode particles with radial grain architectures using stereological generative adversarial networks |
| title_sort | generating multi scale li ion battery cathode particles with radial grain architectures using stereological generative adversarial networks |
| url | https://doi.org/10.1038/s43246-024-00728-5 |
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