Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Accurate reconstruction of the conductive networks in lithium‐ion battery electrodes is essential for understanding material‐structure‐property relationships and optimizing manufacturing processes. The resolution of these networks remains a challenge due to the average size of the microstructure features and lack of contrast to the other materials generally present. Here, a digital materials framework for the three‐dimensional reconstruction and quantitative analysis of lithium‐ion battery cathode microstructure using focused ion beam–scanning electron microscopy (FIB–SEM) tomography with segmentation by machine learning is introduced. Low acceleration voltage image stacks are recorded and compared to identify the optimal voltage. Manual segmentation aided with standard software provides a ground truth that is used to train a deep neural network model to identify the phases of a cathode material. The model is applied for robust identification and reconstruction of conductive phases. Quantified microstructural descriptors are extracted from the automatic segmentation and systematically linked to structured processing parameters within a digital platform, DataCharge.io, enabling consistent comparison across lab‐ and pilot‐scale electrodes. This integrated data‐driven approach facilitates targeted optimization of electrode fabrication, supports transferable process–structure correlations, and advances digitalization strategies for battery materials engineering.