The XRD-CT data were first analysed using the conventional approach; the images were reconstructed using the filtered back projection algorithm and then full profile analysis of the local diffraction patterns was performed using the Rietveld method. This type of analysis can lead to accurate scale factor and weight fraction maps for the various crystalline components in the sample despite the parallax artefacts present in the data. The weight fraction maps obtained from the Rietveld analysis were used to simulate an absorption map using the X-ray mass attenuation coefficient from NIST . This μ attenuation map was then used to correct the XRD-CT sinogram data for self-absorption using the zero-order approximation (Figures S26-28) . This process was followed for all acquired XRD-CT datasets. The self-absorption corrected XRD-CT sinograms were used to reconstruct high quality XRD-CT images (Figures S29-30) using a modified version of the Noise2Inverse denoising self-supervised convolutional neural network . The denoised XRD-CT data were then passed to PQNet models for analysis of the local diffraction patterns. It should be noted that the XRD-CT data contain parallax artefacts due to the large sample size which can introduce errors related to peak position and peak shape. In this work it was not possible to use the DLSR algorithm due to computational limitations i.e. it is not currently computationally feasible to process 637×637 images and rebinning the data to lower resolution leads to unstable results due to the thickness of the electrode layer being very thin. To suppress the parallax artefact, the Q range used for the data analysis in this work was restricted to a region of 0.5-3.85 Å -1 . The differences between the two distinct regions in the NCA lattice parameters maps presented in this work are not parallax related as clearly indicated by the error bars in Figure (i.e. the errors correspond to the standard deviation). Quantitative Rietveld refinement was performed on reconstructed diffraction patterns with the TOPAS v7 software (Figures ). The PQNet was used to analyse the spatially-resolved diffraction patterns present in all XRD-CT datasets presented in this work. Details regarding the network architectures and parameters used are provided in the Supporting Information (Figures ). A pseudo-voigt peak shape function calculated after the analysis of the CeO2 calibrant pattern was used for diffraction data (Figure ). A single pseudo-voigt peak was used to model the peak generated by the semi-crystalline separator component. Uncertainty maps were obtained using the PQ-Net deep ensembles (10 models) and are presented in Figures .