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

arXiv: https://arxiv.org/abs/2407.05333 · HTML: https://mlsanity.com/paper/2407.05333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 66 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Deep Back-Projection Networks For Super-Resolution](https://mlsanity.com/paper/1803.02735/) (2018)
- [Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images](https://mlsanity.com/paper/1901.11153/) (2019)
- [Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks](https://mlsanity.com/paper/2201.03299/) (2022)
- [Generating 3D structures from a 2D slice with GAN-based dimensionality expansion](https://mlsanity.com/paper/2102.07708/) (2021)
- [Generalized balanced power diagrams for 3D representations of polycrystals](https://mlsanity.com/paper/1411.4535/) (2014)
- [Cracking predictions of lithium-ion battery electrodes by X-ray computed tomography and modelling](https://mlsanity.com/paper/2202.09572/) (2022)
- [Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data](https://mlsanity.com/paper/2310.18523/) (2023)

## Abstract (arXiv)

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.