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Abstract C–H functionalization of commodity polyolefins affords functional materials derived from a high‐volume, low‐cost resource. However, current postpolymerization modification strategies result in randomly distributed functionalization along the length of the polymer backbone, which has a negative impact on the crystallinity of the resultant polymers, and thus the thermomechanical properties. Here, we demonstrate an amidyl radical mediated C–H functionalization of polyolefins to access blocky microstructures, which exhibit a higher crystalline fraction, larger crystallite size, and improved mechanical properties compared to their randomly functionalized analogues. Taking inspiration from the site‐selective C–H functionalization of small molecules, we leverage the steric protection provided by crystallites and target polymer functionalization to amorphous domains in a semicrystalline polyolefin gel. The beneficial outcomes of blocky functionalization are independent of the identity of the pendant functional group that is installed through functionalization. The decoupling of functional group incorporation and crystallinity highlights the promise in accessing nonrandom microstructures through selective functionalization to circumvent traditional tradeoffs in postpolymerization modification, with potential impact in advanced materials and upcycling plastic waste. 

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.