The convolutional neural network (CNN) is a widely-used deep learning approach for computer vision tasks, which utilizes image-like data as input. A typical CNN architecture includes the following different layers: convolutional layers, pooling layers, activation functions and fully connected layers. Convolutional layers extract the spatial features of the The resolution of the CNN input in every direction was chosen to be 5 times lower than the grid size adopted for the on-lattice kMC simulations. Such a resolution sufficiently mitigates discretization effects while maintaining a small enough input size to the CNN models for computational efficiency. Accordingly, all microstructures are first converted to a 75 × 75 × 75 binary images, where voxels corresponding to the region occupied by the nanoparticles are denoted as 1, and 0 is used to index the empty voxels (more details in SI, Section S3). Although the on-lattice kMC simulation models cation transport through the The CNN models were trained on 4 NVIDIA Tesla V100 GPUs with 16GB RAM for each GPU, using the Keras library 47 built on top of Tensorflow implemented in Python 3.7. The hyperparameters used to train the CNN models are reported in SI, Section S4.