Polymer Brushes and Grafted Polymers: AI/ML-Driven Synthesis, Simulation, and Characterization towards autonomous SDL

Polymer brushes and grafted polymers have attracted significant interest at the intersection of polymers, interfacial chemistry, colloidal science, and nanostructuring. The confinement of high-density grafted polymers and differences in swelling regimes govern the synthetic challenges and the interesting physics underlying their macromolecular dynamics. In this article, we focus on another intersection, artificial intelligence and machine learning (AI/ML), and how workflows will enhance the microstructure and composition of these systems. It will also accelerate potential applications through high-throughput experimentation (HTE) and data-driven intelligence, enabling scientific discovery and optimization. Applications in microfluidics, sensors, bioimplants, drug delivery, and related areas may yet offer more opportunities for ML-driven optimization. There is also interest in applying these studies with self-driving laboratories (SDLs) that can leverage autonomous systems for synthesis screening, characterization, and application evaluation.