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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. 

Defining the similarity between chemical entities is an essential task in polymer informatics, enabling ranking, clustering, and classification. Despite its importance, the pairwise chemical similarity of polymers remains an open problem. Here, a similarity function for polymers with well-defined backbones is designed based on polymers’ stochastic graph representations generated from canonical BigSMILES, a structurally based line notation for describing macromolecules. The stochastic graph representations are separated into three parts: repeat units, end groups, and polymer topology. The earth mover’s distance is utilized to calculate the similarity of the repeat units and end groups, while the graph edit distance is used to calculate the similarity of the topology. These three values can be linearly or nonlinearly combined to yield an overall pairwise chemical similarity score for polymers that is largely consistent with the chemical intuition of expert users and is adjustable based on the relative importance of different chemical features for a given similarity problem. This method gives a reliable solution to quantitatively calculate the pairwise chemical similarity score for polymers and represents a vital step toward building search engines and quantitative design tools for polymer data. 

The growth of photochemistry and high throughput experimentation in well plates and flow drives interest in photochemical platforms that provide spatially uniform irradiation of reactions. Here, we present a design of a versatile, uniform light platform for photochemistry to enable increased performance and reproducibility for high throughput experimentation in shallow well plates, in-plane flow reactors, and droplets. The design of the platform is driven by the development of an open-source ray tracing light simulation package. Radiometry provides experimental validation of the system's irradiance and irradiance uniformity. The usefulness of the approach is demonstrated by application to the photoinduced electron transfer–reversible addition–fragmentation chain transfer polymerization of methyl acrylate. 

Autonomous experimental systems offer a compelling glimpse into a future where closed-loop, iterative cycles—performed by machines and guided by artificial intelligence (AI) and machine learning (ML)—play a foundational role in materials research and development. This perspective draws attention to the roles of networks and interfaces—of and between humans and machines—for the purpose of generating knowledge and accelerating innovation. Polymers, a class of materials with massive global impact, present a unique opportunity for the application of informatics and automation to pressing societal challenges. To develop these networks and interfaces in polymer science, the Community Resource for Innovation in Polymer Technology (CRIPT)—a polymer data ecosystem based on novel polymer data model, representation, search, and visualization technologies—is introduced. The ongoing co-design efforts engage stakeholders in industry, academia, and government to uncover rapidly actionable, high-impact opportunities to build networks, bridge interfaces, and catalyze innovation in polymer technology. 

Favorable polymer-substrate interactions induce surface orientation fields in block copolymer (BCP) melts. In linear BCP processed near equilibrium, alignment of domains generally persists for a small number of periods (∼4–6 D 0 ) before randomization of domain orientation. Bottlebrush BCP are an emerging class of materials with distinct chain dynamics stemming from substantial molecular rigidity, enabling rapid assembly at ultrahigh (>100 nm) domain periodicities with strong photonic properties (structural color). This work assesses interface-induced ordering in PS- b -PLA bottle b rush diblock copolymer films during thermal annealing between planar surfaces. To clearly observe the decay in orientational order from surface to bulk, we choose to study micron-scale films spanning greater than 200 lamellar periods. In situ optical microscopy and transmission UV-Vis spectroscopy are used to monitor photonic properties during annealing and paired with ex situ UV-Vis reflection measurement, cross-sectional scanning electron microscopy (SEM), and small-angle X-ray scattering (SAXS) to probe the evolution of domain microstructure. Photonic properties were observed to saturate within minutes of annealing at 150 °C, with distinct variation in transmission response as a function of film thickness. The depth of the highly aligned surface region was found to vary stochastically in the range of 30–100 lamellar periods, with the sharpness of the orientation gradient decreasing substantially with increasing film thickness. This observation suggests a competition between growth of aligned, heterogeneously nucleated, grains at the surface and orientationally isotropic, homogeneously nucleated, grains throughout the bulk. This work demonstrates the high potential of bottlebrush block copolymers in rapid fabrication workflows and provides a point of comparison for future application of directed self-assembly to BBCP ordering. 

Additive manufacturing of functional materials is limited by control of microstructure and assembly at the nanoscale. In this work, we integrate nonequilibrium self-assembly with direct-write three-dimensional (3D) printing to prepare bottlebrush block copolymer (BBCP) photonic crystals (PCs) with tunable structure color. After varying deposition conditions during printing of a single ink solution, peak reflected wavelength for BBCP PCs span a range of 403 to 626 nm (blue to red), corresponding to an estimated change in d-spacing of >70 nm (Bragg- Snell equation). Physical characterization confirms that these vivid optical effects are underpinned by tuning of lamellar domain spacing, which we attribute to modulation of polymer conformation. Using in situ optical microscopy and solvent-vapor annealing, we identify kinetic trapping of metastable microstructures during printing as the mechanism for domain size control. More generally, we present a robust processing scheme with potential for on-the-fly property tuning of a variety of functional materials.