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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.17171 |
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| _version_ | 1866912286036197376 |
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| author | Furat, Orkun Weber, Sabrina Schubert, Johannes Rekers, René Luczak, Maximilian Glatt, Erik Wiegmann, Andreas Janek, Jürgen Bielefeld, Anja Schmidt, Volker |
| author_facet | Furat, Orkun Weber, Sabrina Schubert, Johannes Rekers, René Luczak, Maximilian Glatt, Erik Wiegmann, Andreas Janek, Jürgen Bielefeld, Anja Schmidt, Volker |
| contents | This paper presents a computational method for generating virtual 3D morphologies of functional materials using low-parametric stochastic geometry models, i.e., digital twins, calibrated with 2D microscopy images. These digital twins allow systematic parameter variations to simulate various morphologies, that can be deployed for virtual materials testing by means of spatially resolved numerical simulations of macroscopic properties. Generative adversarial networks (GANs) have gained popularity for calibrating models to generate realistic 3D morphologies. However, GANs often comprise of numerous uninterpretable parameters make systematic variation of morphologies for virtual materials testing challenging. In contrast, low-parametric stochastic geometry models (e.g., based on Gaussian random fields) enable targeted variation but may struggle to mimic complex morphologies. Combining GANs with advanced stochastic geometry models (e.g., excursion sets of more general random fields) addresses these limitations, allowing model calibration solely from 2D image data. This approach is demonstrated by generating a digital twin of all-solid-state battery (ASSB) cathodes. Since the digital twins are parametric, they support systematic exploration of structural scenarios and their macroscopic properties. The proposed method facilitates simulation studies for optimizing 3D morphologies, benefiting not only ASSB cathodes but also other materials with similar structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17171 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative adversarial framework to calibrate excursion set models for the 3D morphology of all-solid-state battery cathodes Furat, Orkun Weber, Sabrina Schubert, Johannes Rekers, René Luczak, Maximilian Glatt, Erik Wiegmann, Andreas Janek, Jürgen Bielefeld, Anja Schmidt, Volker Machine Learning This paper presents a computational method for generating virtual 3D morphologies of functional materials using low-parametric stochastic geometry models, i.e., digital twins, calibrated with 2D microscopy images. These digital twins allow systematic parameter variations to simulate various morphologies, that can be deployed for virtual materials testing by means of spatially resolved numerical simulations of macroscopic properties. Generative adversarial networks (GANs) have gained popularity for calibrating models to generate realistic 3D morphologies. However, GANs often comprise of numerous uninterpretable parameters make systematic variation of morphologies for virtual materials testing challenging. In contrast, low-parametric stochastic geometry models (e.g., based on Gaussian random fields) enable targeted variation but may struggle to mimic complex morphologies. Combining GANs with advanced stochastic geometry models (e.g., excursion sets of more general random fields) addresses these limitations, allowing model calibration solely from 2D image data. This approach is demonstrated by generating a digital twin of all-solid-state battery (ASSB) cathodes. Since the digital twins are parametric, they support systematic exploration of structural scenarios and their macroscopic properties. The proposed method facilitates simulation studies for optimizing 3D morphologies, benefiting not only ASSB cathodes but also other materials with similar structures. |
| title | Generative adversarial framework to calibrate excursion set models for the 3D morphology of all-solid-state battery cathodes |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.17171 |