Minh-Quyet Ha

Abstract Developing efficient catalysts for proton exchange membrane fuel cells (PEMFCs) requires minimising platinum (Pt) usage while enhancing oxygen reduction reaction (ORR) activity. While density functional theory (DFT) calculations provide valuable structural insights, understanding complex structure-property relationships often requires extensive manual analysis.

Abstract This paper introduces a deep learning (DL)-based method for phase retrieval tailored to single-shot, multiple-frame coherent X-ray diffraction imaging (CXDI), designed specifically for visualizing local nanostructural dynamics within a larger sample. Current phase retrieval methods often struggle with achieving high spatiotemporal resolutions, handling dynamic imaging, and managing computational costs, which limits their applicability in observing nanostructural dynamics.

Abstract Deep learning (DL) models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material properties. To address these limitations, we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property relationships.