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Upcycling plastic waste into reprocessable materials with performance-advantaged properties would contribute to the development of a circular plastics economy. Here, we modify branched polyolefins and postconsumer polyethylene through a versatile C−H functionalization approach using thiosulfonates as a privileged radical group transfer functionality. Cross-linking the functionalized polyolefins with polytopic amines provided dynamically cross-linked polyolefin networks enabled by associative bond exchange of diketoenamine functionality. A combination of resonant soft X-ray scattering and grazing incidence X-ray scattering revealed hierarchical phase morphology in which diketoenamine-rich microdomains phase-separate within amorphous regions between polyolefin crystallites. The combination of dynamic covalent cross-links and microphase separation results in useful and improved mechanical properties, including a ∼4.5-fold increase in toughness, a reduction in creep deformation at temperatures relevant to use, and high-temperature structural stability compared to the parent polyolefin. The dynamic nature of diketoenamine cross-links provides stress relaxation at elevated temperatures, which enabled iterative reprocessing of the dynamic covalent polymer network with little cycle-to-cycle property fade. The ability to convert polyolefin waste into a reprocessable thermoformable material with attractive thermomechanical properties provides additional optionality for upcycling to enable future circularity. 

Abstract The development of high‐performance elastomers for additive manufacturing requires overcoming complex property trade‐offs that challenge conventional material discovery pipelines. Here, a human‐in‐the‐loop reinforcement learning (RL) approach is used to discover polyurethane elastomers that overcome pervasive stress–strain property tradeoffs. Starting with a diverse training set of 92 formulations, a coupled multi‐component reward system was identified that guides RL agents toward materials with both high strength and extensibility. Through three rounds of iterative optimization combining RL predictions with human chemical intuition, we identified elastomers with more than double the average toughness compared to the initial training set. The final exploitation round, aided by solubility prescreening, predicted twelve materials exhibiting both high strength (>10 MPa) and high strain at break (>200%). Analysis of the high‐performing materials revealed structure‐property insights, including the benefits of high molar mass urethane oligomers, a high density of urethane functional groups, and incorporation of rigid low molecular weight diols and unsymmetric diisocyanates. These findings demonstrate that machine‐guided, human‐augmented design is a powerful strategy for accelerating polymer discovery in applications where data is scarce and expensive to acquire, with broad applicability to multi‐objective materials optimization.