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A mussel-inspired mechanism was used to solve the problem of filler aggregation in rubber composites. This research aims to improve carbon black (CB) dispersion in epoxidized natural rubber (ENR) composites through π−π stacking and cation−π interactions by adding dopamine (D). In this study, various aromatic interactions (π−π stacking and cation−π interactions) between the D-functionalized ENR molecules and the surface of the CB were observed by Fourier transform infrared (FTIR) and Raman spectroscopy. Notably, the small and wideangle X-ray scattering (SAXS/WAXS) analyses supported our inference from the rubber processing analysis (RPA) and transmission electron microscopy (TEM) results that the aromatic interactions enhanced the CB dispersion in ENR composites. This phenomenon improved the tensile strength (138%), Young’s modulus (93%), and energy-saving properties (50%). Finally, this research provided an alternative strategy using mussel-inspired material to solve the CB aggregation problem in rubber products, yielding ENR composites with superior performance properties. 

ABSTRACT Block copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials.