Microstructure of electrodes determines the performance of electrochemical devices such as fuel cells and batteries. The efficiency and economic feasibility of these technologies depend on the stability of the microstructures throughout their lifetime. Although modeling techniques were proposed for determining electrode performance from 2- or 3-dimensional microstructural data, it is still extremely challenging to predict long-term structural degradation by means of numerical simulations. One of the major challenges is to overcome the difficulties in obtaining experimental data of an identical sample through the degradation process. In this work, a machine learning-based framework for predicting microstructural evolutions with limited amount of un-paired training data is proposed. Physically-constrained unsupervised image-to-image translation (UNIT) network is incorporated to predict nickel oxide reduction process in solid oxide fuel cell anode. The proposed framework is firstly validated by simplified toy-problems. Secondly, the UNIT network is applied to real microstructures of solid oxide fuel cells, which results in excellent visual and statistical agreements between real and artificially reduced samples. The proposed network can predict evolutions in new microstructures, which have not been used during training. Furthermore, a conditional UNIT network (C-UNIT) was demonstrated, which can predict the microstructure evolutions based on process conditions as well as continuous time series of microstructural changes.

电极的微观结构决定了燃料电池和电池等电化学装置的性能。这些技术的效率和经济可行性取决于微观结构在其整个生命周期内的稳定性。尽管提出了根据 2 维或 3 维微观结构数据确定电极性能的建模技术，但通过数值模拟来预测长期结构退化仍然极具挑战性。主要挑战之一是克服通过降解过程获得相同样品的实验数据的困难。在这项工作中，提出了一种基于机器学习的框架，用于使用有限数量的未配对训练数据来预测微观结构演化。结合物理约束的无监督图像到图像转换（UNIT）网络来预测固体氧化物燃料电池阳极中的氧化镍还原过程。所提出的框架首先通过简化的玩具问题进行验证。其次，UNIT 网络应用于固体氧化物燃料电池的真实微观结构，这使得真实样品和人工还原样品之间具有出色的视觉和统计一致性。所提出的网络可以预测训练期间尚未使用的新微观结构的演变。此外，还演示了条件 UNIT 网络（C-UNIT），它可以根据工艺条件以及微观结构变化的连续时间序列预测微观结构的演变。