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Functional properties of transition-metal oxides strongly depend on crystallographic defects; crystallographic lattice deviations can affect ionic diffusion and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging of local structural distortions across an extended spatial region of thin samples. Yet, localized lattice distortions remain challenging to detect and localize using nanodiffraction, due to their weak diffuse scattering. Here, we apply an unsupervised machine learning clustering algorithm to isolate the low-intensity diffuse scattering in as-grown and alkaline-treated thin epitaxially strained SrIrO3 films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SrIrO3, and interpret the defect type by analyzing the diffraction profile through clustering. Our findings demonstrate the use of a machine learning clustering algorithm for identifying and characterizing hard-to-find crystallographic defects in thin films of electrocatalysts and highlight the potential to study electrochemical reactions at defect sites in operando experiments.

Non‐equilibrium defects often dictate the macroscopic properties of materials. They largely define the reversibility and kinetics of processes in intercalation hosts in rechargeable batteries. Recently, imaging methods have demonstrated that transient dislocations briefly appear in intercalation hosts during ion diffusion. Despite new discoveries, the understanding of impact, formation and self‐healing mechanisms of transient defects, including and beyond dislocations, is lacking. Here, operando X‐ray Bragg Coherent Diffractive Imaging (BCDI) and diffraction peak analysis capture the stages of formation of a unique metastable domain boundary, defect self‐healing, and resolve the local impact of defects on ionic diffusion in Na_xNi_1−yMn_yO₂intercalation hosts in a charging sodium‐ion battery. Results, applicable to a wide range of layered intercalation materials due to the shared nature of framework layers, elucidate new dynamics of transient defects and their connection to macroscopic properties, and suggest how to control the nanostructure dynamics.

Structural and ion‐ordering phase transitions limit the viability of sodium‐ion intercalation materials in grid scale battery storage by reducing their lifetime. However, the combination of phenomena in nanoparticulate electrodes creates complex behavior that is difficult to investigate, especially on the single‐nanoparticle scale under operating conditions. In this work, operando single‐particle X‐ray diffraction (oSP‐XRD) is used to observe single‐particle rotation, interlayer spacing, and layer misorientation in a functional sodium‐ion battery. oSP‐XRD is applied to Na_2/3[Ni_1/3Mn_2/3]O₂, an archetypal P2‐type sodium‐ion‐positive electrode material with the notorious P2‐O2 phase transition induced by sodium (de)intercalation. It is found that during sodium extraction, the misorientation of crystalline layers inside individual particles increases before the layers suddenly align just prior to the P2‐O2 transition. The increase in the long‐range order coincides with an additional voltage plateau signifying a phase transition prior to the P2‐O2 transition. To explain the layer alignment, a model for the phase evolution is proposed that includes a transition from localized to correlated Jahn–Teller distortions. The model is anticipated to guide further characterization and engineering of sodium‐ion intercalation materials with P2‐O2 type transitions. oSP‐XRD, therefore, opens a powerful avenue for revealing complex phase behavior in heterogeneous nanoparticulate systems.