| Abstract Scope |
We address the challenges in characterizing mesoscale materials dynamics across diverse length and time scales, with the focus on enabling informed multi-scale experiments at advanced facilities. Current limitations in data inversion processes hinder real-time feedback, constraining our ability to efficiently identify regions of interest during an experiment to guide multiscale measurements. We will explore the application of machine learning (ML) for reconstructing crystal orientation from diffraction data, adaptively tuning ML models for improved predictions, and incorporating physics knowledge to develop more robust ML models. The primary objective is to facilitate multiscale materials characterization experiments at beamlines, providing real-time feedback and reducing data analysis time. By addressing these challenges, we aim to improve the efficiency and effectiveness of multiscale experiments, ultimately leading to a deeper understanding of material dynamics and promoting advancements in mesoscale materials science. |