Creating and curating new data appends the way we approach materials science. In additive manufacturing (AM), the fabrication of parts and objects with high complexity and high performance is advantageous over other methods. Using nanocomposites enables highly improved properties even with “commodity polymers” that do not need to undergo high-temperature processes or extensive reformulation. With artificial intelligence and machine learning (AI/ML), optimizing the formulation and manufacturing methods is possible. Using sensors capable of a feedback loop mechanism and the ability to use simulation to create digital twins, optimizing properties in record time is possible. Statistical and logic-derived design, including regression analysis, are starting points for designing experiments (DOE) or principal component analysis(PCA) in optimization and analysis vs trial-and-error approaches when working with polymer materials. In this talk, we demonstrate the approaches toward understanding Nanostructuring in composites and hierarchical approaches in optimization via AI/ML and other training/learning sets for specific properties and applications, such as 3D printing and flow chemistry reactions. Introducing more sensors (monitoring instruments) in AM processes and real-time ML with online monitoring allows a feedback loop and deep learning (DL) for autonomous fabrication and data analytics.

Nanopatterned interfaces enable precise control over surface morphology and chemistry at the nanoscale, offering advanced capabilities in biosensing, molecular capture, and adaptive surface engineering. Their high-aspect-ratio structures enhance film integrity and allow spatially discrete functional domains. When combined with stimuli-responsive polymers, these surfaces can respond dynamically to environmental cues[1]. However, most existing systems incorporate only one type of responsive polymer, limiting their functionality and versatility[2]. Challenges in fabrication and chemical compatibility have hindered the integration of multiple responsive components into a single nanoscale interface. Recent advances in nanolithographic templating and surface-initiated photoinduced electron transfer-reversible addition–fragmentation chain transfer (SI-PET-RAFT) polymerization have enabled the creation of binary-patterned surfaces with independent spatial and chemical control[3]. We constructed a dual-responsive nanopatterned interface by integrating photothermal polypyrrole (PPy) with thermoresponsive poly(EGMEA-co-PEGMEA) brushes[4]. Nanoporous PPy films were prepared via colloidal templating and electrochemical deposition, followed by selective brush growth through SI-PET-RAFT polymerization. This binary system demonstrates the synergistic potential of combining multiple responsive elements within confined nanostructures. It offers a modular platform for multifunctional surfaces with applications in biosensing, targeted capture, and smart biointerfaces.