Shape memory elastomers (SMEs) are a class of intelligent materials characterized by their ability to deform and recover shapes under applied force and external stimuli. Heat and ultraviolet radiation are examples of the most common external stimuli. With the emerging prevalence of internet of things devices and the ensuing need for smart materials and structures, SMEs provide significant opportunities to support the development of novel applications in robotics, remotely actuated systems, and packages, including those promised for the space industry. To harness the immense potential in the emerging applications of these materials, one approach is the systematic multi‐scale modeling coupled with artificial intelligence‐assisted design leading to the development of next‐generation intelligent systems. This review covers several aspects of the synthesis/materials chemistry and applications of SMEs with a view towards enabling such an approach. The synthesis procedures emphasizing dynamic covalent bond reactions are reviewed. Then, liquid crystalline elastomers are introduced as a specific elastomeric material class that exhibits excellent shape memory characteristics and distinctive transition temperatures. The utilization of advanced manufacturing methods such as additive manufacturing, three‐dimensional printing, and the emerging four‐dimensional printing technologies assisted by machine learning are detailed in producing and predicting SMEs. Finally, the current trends in the use of SMEs are summarized in areas of industrial and space engineering and biomedical applications.

In this research, multiple-shape memory polymers were prepared from benzoxazine (BA-a) resin and a urethane prepolymer (PU). The effects of BA-a resin content on the thermal, mechanical and multiple-shape memory properties were investigated by differential scanning calorimetry (DSC), thermogravimetric analysis, dynamic mechanical analysis, a flexural test and a multiple-shape memory test. The results revealed that the suitable curing conversions of BA-a:PU resin mixtures affect the shape memory behaviors. The BA-a/PU copolymers demonstrated an increase in flexural strength and flexural modulus at various deformation temperatures with an increase in the BA-a mass ratio from 55%–80%, whereas the thermal properties of these binary systems, i.e. glass transition temperature (Tg), degradation temperature (Td) and char yield, were also found to increase with an increase in BA-a content. In addition, the two-step bending test was carried out using a universal testing machine to evaluate the multiple-shape memory properties. The results revealed that the BA-a/PU samples exhibited high values of shape fixity (70%–96% for the first temporary shape and 83%–99% for the second temporary shape) and shape recovery (88%–96% for the first temporary shape and 97%–99% for the original shape).

Molecular dynamic (MD) simulation techniques are increasingly being adopted as efficient computational tools to design novel and exotic classes of materials for which traditional methods of synthesis and prototyping are either too costly, unsafe, and time-consuming in laboratory settings. Of such class of materials are liquid crystalline elastomers (LCEs) with favorable shape memory characteristics. These materials exhibit some distinct properties, including stimuli responsiveness to heat or UV and appropriate molecular structure for shape memory behaviors. In this work, the MD simulations were employed to compare and assess the leading force fields currently available for modeling the behavior of a typical LCE system. Three force fields, including Dreiding, PCFF, and SciPCFF, were separately assigned to model the LCE system, and their suitability was validated through experimental results. Among these selected force fields, the SciPCFF produced the best agreement with the experimentally measured thermal and viscoelastic properties compared to those of the simulated steady-state density, transition temperature, and viscoelastic characteristics. Next, shape fixity (R f ) and shape recovery (R r ) of LCEs were estimated using this force field. A fourstep simulated shape memory procedure proceeded under a tensile mode. The changes in molecular conformations were calculated for R f and R r after the unloading step and reheating step. The results revealed that the model LCE system exhibit characteristic behaviors of Rf and Rr over the thermomechanical shape memory process, confirming the suitability of selected force field for use in the design and prediction of properties of typical LCE class of polymers. I. INTRODUCTIONShape memory elastomers (SMEs) are a significant member of intelligent polymers which can be deformed to another temporary shape and subsequently recovered to their permanent shape again. 1,2 This process is typically achieved through an applied load and forms of trigger by an external stimulus. 3 Currently, a variety of external stimuli are utilized to activate shape memory effects, including heat 4 , pH 5 , water 6 , UV light 7 , or electric and magnetic fields 8,9 . Among these, heat is the most commonly used stimulus. Furthermore, molecular architecture of these materials, containing net-points (hard phase) and switch units (soft phase), is the crucial determining factor which promotes the shape memory behavior. 10 The net-points responsible for maintaining the permanent shape are generated by covalent

Liquid crystalline elastomers (LCEs) are stimuli-responsive materials with potential use in shape memory applications. Though particularly suited for shape memory, the LCEs however have some drawbacks such as low shape fixity (R f ) and slow recovery time. To overcome these limitations, new lignin-filled elastomeric liquid crystalline (ELC) composite materials were fabricated. The lignin used is a by-product of Kraft pulping process, which was obtained from renewable resources abundant in nature. Here, we show that the aromatic structure of lignin increases the netpoints density at the microscopic level in the ELC composite systems. Shape memory effects are enhanced by incorporating up to only 7 wt% of lignin, resulting in an R f of 97% for the composites. Concurrently, these composites were able to maintain their shape recovery (R r ) of nearly 100%. The recovery time of the composites reduces with increasing lignin content due to the higher elastic energy released from the netpoints based on the lignin structure. The ELC composites with 7 wt% lignin could fully recover within 70 s, while the neat LCE counterpart took 100 s. Morphological features of dispersed lignin shows that even without surface modification and only a moderate quality of dispersion in LCE matrix, both shape memory and dynamic mechanical properties of the resultant composites can be significantly improved.

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