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The BigSMILES notation, a concise tool for polymer ensemble representation, is augmented here by introducing an enhanced version called generative BigSMILES. G-BigSMILES is designed for generative workflows, and is complemented by tailored software tools for ease of use. This extension integrates additional data, including reactivity ratios (or connection probabilities among repeat units), molecular weight distributions, and ensemble size. An algorithm, interpretable as a generative graph is devised that utilizes these data, enabling molecule generation from defined polymer ensembles. Consequently, the G-BigSMILES notation allows for efficient specification of complex molecular ensembles via a streamlined line notation, thereby providing a foundational tool for automated polymeric materials design. In addition, the graph interpretation of the G-BigSMILES notation sets the stage for robust machine learning methods capable of encapsulating intricate polymeric ensembles. The combination of G-BigSMILES with advanced machine learning techniques will facilitate straightforward property determination and in silico polymeric material synthesis automation. This integration has the potential to significantly accelerate materials design processes and advance the field of polymer science. 

Abstract Dynamic liquid crystal elastomers (LCEs) are a class of polymer networks characterized by the inclusion of both liquid crystalline monomers and dynamic covalent bonds. The unique properties realized through the combination of these moieties has produced a plethora of stimuli‐responsive materials to address a range of emerging technologies. While previous works have studied the incorporation of different dynamic bonds in LCEs, few (if any) have studied the effect of the specific placement of the dynamic bonds within an LCE network. A series of dynamic LCE networks were synthesized using a generalizable approach that employs a tandem thiol‐ene/yne chemistry which allows the location of the dynamic disulfide bond to be varied while maintaining similar network characteristics. When probing these systems in the LC regime, the thermomechanical properties were found to be largely similar. It is not until elevated temperatures (160–180 °C) that differences in the relaxation activation energies of these systems begin to materialize based solely on differences in placement of the dynamic bond throughout the network. This work demonstrates that through intentional dynamic bond placement, stress relaxation times can be tuned without affecting the LCE character. This insight can help optimize future dynamic LCE designs and achieve shorter processing times. 

Fluoroether solvents are promising electrolyte candidates for high-energy-density lithium metal batteries, where high ionic conductivity and oxidative stability are important metrics for design of new systems. Recent experiments have shown that these performance metrics, particularly stability, can be tuned by changing the fraction of ether and fluorine content. However, little is known about how different molecular architectures influence the underlying ion transport mechanisms and conductivity. Here, we use all-atom molecular dynamics simulations to elucidate the ion transport and solvation characteristics of fluoroether chains of varying length, and having different ether segment and fluorine terminal group contents. The design rules that emerge from this effort are that solvent size determines lithium-ion transport kinetics, solvation structure, and solvation energy. In particular, the mechanism for lithium-ion transport is found to shift from ion hopping between solvation sites located in different fluoroether chains in short-chain solvents, to ion–solvent co-diffusion in long-chain solvents, indicating that an optimum exists for molecules of intermediate length, where hopping is possible but solvent diffusion is fast. Consistent with these findings, our experimental measurements reveal a non-monotonic behavior of the effects of solvent size on lithium-ion conductivity, with a maximum occurring for medium-length solvent chains. A key design principle for achieving high ionic conductivity is that a trade-off is required between relying on shorter fluoroether chains having high self-diffusivity, and relying on longer chains that increase the stability of local solvation shells.