Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data
This work addresses the data processing bottleneck for researchers studying perovskite crystallization in solar cells, offering an incremental improvement over traditional methods.
The authors tackled the challenge of analyzing large amounts of real-time X-ray scattering data for perovskite crystallization by developing an automated pipeline using a modified Faster R-CNN deep learning model, which achieved high accuracy in detecting diffraction features on noisy patterns and enabled automated phase identification and fast tracking of perovskite formation.
Understanding the processes of perovskite crystallization is essential for improving the properties of organic solar cells. In situ real-time grazing-incidence X-ray diffraction (GIXD) is a key technique for this task, but it produces large amounts of data, frequently exceeding the capabilities of traditional data processing methods. We propose an automated pipeline for the analysis of GIXD images, based on the Faster R-CNN deep learning architecture for object detection, modified to conform to the specifics of the scattering data. The model exhibits high accuracy in detecting diffraction features on noisy patterns with various experimental artifacts. We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications: 1. the automated phase identification and unit-cell determination of two coexisting phases of Ruddlesden-Popper 2D perovskites, and 2. the fast tracking of MAPbI$_3$ perovskite formation. By design, our approach is equally suitable for other crystalline thin-film materials.