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Abstract Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions. 

Abstract Most elastomers undergo strain‐induced crystallization (SIC) under tension; as individual chains are held rigidly in a fixed position by an applied strain, their alignment along the strain field results in a shift from strain‐hardening (SH) to SIC. A similar degree of stretching is associated with the tension necessary to accelerate mechanically coupled, covalent chemical responses of mechanophores in overstretched chains, raising the possibility of an interplay between the macroscopic response of SIC and the molecular response of mechanophore activation. Here, thiol‐yne‐derived stereoelastomers doped covalently with a dipropiolate‐derivatized spiropyran (SP) mechanophore (0.25–0.38 mol%) are reported. The material properties of SP‐containing films are consistent with undoped controls, indicating that the SP is a reporter of the mechanical state of the polymer. Uniaxial tensile tests reveal correlations between mechanochromism and SIC, which are strain‐rate‐dependent. When mechanochromic films are stretched slowly to the point of mechanophore activation, the covalently tethered mechanophore remains trapped in a force‐activated state, even after the applied stress is removed. Mechanophore reversion kinetics correlate with the applied strain rate, resulting in highly tunable decoloration rates. Because these polymers are not covalently crosslinked, they are recyclable by melt‐pressing into new films, increasing their potential range of strain‐sensing, morphology‐sensing, and shape‐memory applications.