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.

翻译：本文提出一种计算框架，通过低参数随机几何模型（即数字孪生体）生成功能材料的三维虚拟形貌，并利用二维显微图像进行标定。这些数字孪生体支持系统性的参数调整以模拟不同形貌，进而可通过空间分辨的宏观性能数值模拟实现虚拟材料测试。生成对抗网络（GANs）已广泛用于模型标定以生成逼真的三维形貌，但其通常包含大量不可解释参数，难以在虚拟材料测试中实现形貌的系统性调控。相比之下，低参数随机几何模型（如基于高斯随机场）虽支持定向调控，却难以复现复杂形貌。将GANs与先进随机几何模型（如更广义随机场的偏移集）相结合可突破这些局限，实现仅依靠二维图像数据的模型标定。本研究以全固态电池（ASSB）阴极的数字孪生构建为例验证该方法。由于数字孪生具有参数化特性，其支持对结构场景及其宏观性能的系统性探索。所提方法为优化三维形貌的模拟研究提供了有效工具，不仅适用于ASSB阴极，也可推广至其他具有类似结构的材料体系。