The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices. This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data. The same network architecture is successfully applied to two very different three-phase microstructures: A lithium-ion battery cathode and a solid oxide fuel cell anode. A comparison between the real and synthetic data is performed in terms of the morphological properties (volume fraction, specific surface area, triple-phase boundary) and transport properties (relative diffusivity), as well as the two-point correlation function. The results show excellent agreement between datasets and they are also visually indistinguishable. By modifying the input to the generator, we show that it is possible to generate microstructure with periodic boundaries in all three directions. This has the potential to significantly reduce the simulated volume required to be considered “representative” and therefore massively reduce the computational cost of the electrochemical simulations necessary to predict the performance of a particular microstructure during optimisation.

多相多孔电极微结构的产生是优化电化学储能装置的关键步骤。这项工作实现了一个深层卷积生成对抗网络（DC-GAN），用于生成逼真的n相微观结构数据。相同的网络体系结构已成功应用于两个截然不同的三相微结构：锂离子电池阴极和固体氧化物燃料电池阳极。根据形态特性（体积分数，比表面积，三相边界）和传输特性（相对扩散率）以及两点相关函数对真实数据和合成数据进行比较。结果表明数据集之间的一致性很好，并且在视觉上也无法区分。通过修改生成器的输入，我们表明，有可能生成在所有三个方向上具有周期性边界的微结构。这有可能显着减少被认为是“代表性的”所需的模拟量，因此可大大减少预测优化过程中特定微结构的性能所必需的电化学模拟的计算成本。