Polyelectrolytes and Water-Soluble Polymers: AI/ML-Driven Materials and Applications

Abstract

Water-soluble polymers are a class of macromolecules that includes natural, synthetic, and polyelectrolyte polymers. Their study is fascinating, and increasingly, new applications are being discovered from membranes to drug delivery. How do we apply artificial intelligence and machine learning (AI/ML) to this class of polymers? An AI/ML-polymer redesign and the application of large language models (LLMs) for agentic AI tasks can transform these macromolecules into libraries and workflows that can answer some of the most interesting questions in microstructure, phase stability, ion transport, and self-assembly, furthering discovery science and new technologies. This article examines the background of natural polymers, water-soluble polymers, polyelectrolytes, polyelectrolyte complexes (PEC), and polyelectrolyte multilayers (PEM). Active ML and creative materials design by generative AI will create opportunities for new structure-composition-processing-property (SCPP) correlations. Methods for scaling synthesis using continuous-flow chemistry (CFC) in autonomous, self-driving labs (SDL) will be examined for parametrization and optimization in reaction engineering. Finally, 3D printing, an advanced manufacturing method, will be reviewed, with a focus on these materials. Perspectives on the future impact of this approach will generate high interest in accelerating research and scientific discovery.