Aileen Luo1,Oleg Gorobtsov1,Jocienne Nelson1,Ding-Yuan Kuo1,Tao Zhou2,Ziming Shao1,Ryan Bouck1,Mathew Cherukara2,Martin Holt2,Kyle Shen1,3,Darrell Schlom1,3,4,Jin Suntivich1,Andrej Singer1
Cornell University1,Argonne National Laboratory2,Kavli Institute at Cornell for Nanoscale Science3,Leibniz-Institut für Kristallzüchtung4
Aileen Luo1,Oleg Gorobtsov1,Jocienne Nelson1,Ding-Yuan Kuo1,Tao Zhou2,Ziming Shao1,Ryan Bouck1,Mathew Cherukara2,Martin Holt2,Kyle Shen1,3,Darrell Schlom1,3,4,Jin Suntivich1,Andrej Singer1
Cornell University1,Argonne National Laboratory2,Kavli Institute at Cornell for Nanoscale Science3,Leibniz-Institut für Kristallzüchtung4
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 due, for example, to line defects 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 SrIrO<sub>3</sub><i> </i>films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SrIrO<sub>3</sub>, 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.