Nano-Computed Tomography (nano-CT) images obtained from an electrode containing LiNi 0.6 Mn 0.2 Co 0.2 O 2 are used to extract realistic particle shapes of the AM. Such images provide high-resolution data of the AM skeleton and allow the visualization of pores in the microstructure, which is an important aspect to consider for our modeling framework validation. Note that this is not the same material as used in the slurry formulation described previously. We chose these nano-CT images on account of the data availability, which is sufficient for the proof-of-concept work reported in this article. A description of the methodology used to extract particle morphologies briefly ensues, it is based on a similar methodology employed for previous models developed in our group. The raw three-dimensional nano-CT images are fed into ImageJ . An example of a two-dimensional slice of the nano-CT image is shown in Figure . Different gray scale values in the image correspond to different material phases. A Weka segmentation plugin is trained to classify each pixel according to its gray scale into its corresponding phase. A Fast Random Forest classifier is used for classification. Carbon additives, binder and solid electrolyte were indistinguishable to nano-CT imaging from one another, which limits the use of realistic SE particle shapes in our simulations. Therefore, in total, three different phases were identified. They correspond to an AM phase, a pore phase and a phase consisting of SE, carbon black and binder. After the segmentation is completed, the AM particles are extracted. A schematic representing this process is shown in Figure . On account of the segmentation, high-resolution images of the AM can be extracted from the nano-CT data and used in subsequent simulations. As a proof-of-concept, and with the purpose of demonstrating the methodology introduced in the present article, the high-resolution particles were not used in the final simulations. Due to the high computational cost of simulating the high-resolution AM, a pixel position averaging technique was employed.