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Abstract Nature excels in both self-healing and 3D shaping; for example, self-healable human organs feature functional geometries and microstructures. However, tailoring man-made self-healing materials into complex structures faces substantial challenges. Here, we report a paradigm of photopolymerization-based additive manufacturing of self-healable elastomer structures with free-form architectures. The paradigm relies on a molecularly designed photoelastomer ink with both thiol and disulfide groups, where the former facilitates a thiol-ene photopolymerization during the additive manufacturing process and the latter enables a disulfide metathesis reaction during the self-healing process. We find that the competition between the thiol and disulfide groups governs the photocuring rate and self-healing efficiency of the photoelastomer. The self-healing behavior of the photoelastomer is understood with a theoretical model that agrees well with the experimental results. With projection microstereolithography systems, we demonstrate rapid additive manufacturing of single- and multimaterial self-healable structures for 3D soft actuators, multiphase composites, and architected electronics. Compatible with various photopolymerization-based additive manufacturing systems, the photoelastomer is expected to open promising avenues for fabricating structures where free-form architectures and efficient self-healing are both desirable. 

Abstract Hydrogels and polydimethylsiloxane (PDMS) are complementary to each other, since the hydrophobic PDMS provides a more stable and rigid substrate, while the water‐rich hydrogel possesses remarkable hydrophilicity, biocompatibility, and similarity to biological tissues. Herein a transparent and stretchable covalently bonded PDMS‐hydrogel bilayer (PHB) structure is prepared via in situ free radical copolymerization of acrylamide and allylamine‐exfoliated‐ZrP (AA‐e‐ZrP) on a functionalized PDMS surface. The AA‐e‐ZrP serves as cross‐linking nano‐patches in the polymer gel network. The covalently bonded structure is constructed through the addition reaction of vinyl groups of PDMS surface and monomers, obtaining a strong interfacial adhesion between the PDMS and the hydrogel. A mechanical‐responsive wrinkle surface, which exhibs transparency change mechanochromism, is created via introducing a cross‐linked polyvinyl alcohol film atop the PHB structure. A finite element model is implemented to simulate the wrinkle formation process. The implication of the present finding for the interfacial design of the PHB and PDMS‐hydrogel‐PVA trilayer (PHPT) structures is discussed. 

Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.