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Neutron scattering techniques are powerful tools for characterizing the structure and dynamics of materials. They are particularly well-suited for studying polymer systems, which are typically rich in hydrogen. By combining neutron scattering with deuterium labeling, researchers can unravel complex structural features and dynamic behaviors within these systems. This review highlights recent advances in neutron scattering methods for probing the hierarchical structures and dynamics of polymeric materials, with a focus on developments over the past decade. We begin by discussing elastic techniques—such as small-angle neutron scattering (SANS)—used to examine polymer organization in both solution and solid states. Subsequently, we addressed the application of neutron reflectometry (NR) and grazing-incidence neutron scattering (GINS) techniques to the study of polymer thin-film structures. Next, we explore inelastic and quasi-elastic techniques, including inelastic neutron scattering (INS), quasi-elastic neutron scattering (QENS), and neutron spin echo (NSE), which provide insight into polymer dynamics across a broad range of time and length scales. Finally, we consider future directions for neutron scattering in soft matter research, emphasizing emerging methodologies and next-generation neutron sources that promise to further advance our understanding of these complex systems. 

Supramolecular polymer blends (SPBs) represent a versatile class of polymers whose morphology directly determines their macroscopic properties. However, rational design of SPBs remains hindered by the lack of predictive models describing how molecular features and intermolecular interactions determine morphology. Here, we report a data-driven high-throughput workflow integrating modular synthesis, robotic sample formulation and processing, automated morphology characterization, and machine learning (ML) for SPBs discovery. Using a plug-and-play modular synthetic strategy, 33 hydrogen-bonding end-functional homopolymer precursors were prepared and orthogonally paired to fabricate 260 SPBs within one day. A custom automated atomic force microscopy (AFM) protocol enabled systematic morphological characterization, producing 2340 images with little human intervention. Average phase separation sizes (e.g. domain spacings) was extracted from processed AFM data using multiple complementary approaches and applied to ML model training. Leveraging the high-throughput sample formation and characterization, a high-quality database was curated for SPBs, allowing training of ML models. Guided by support vector regression (SVR) model, target morphologies of 50, 100, and 150 nm were successfully predicted and experimentally validated. This work demonstrates the potential of coupling high-throughput experimentation with ML to accelerate polymer blends phase discovery, providing one of the first large-scale, experimentally derived datasets specifically designed for supramolecular polymer research.