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

TL;DR

This paper explores how AI and machine learning can revolutionize the synthesis, simulation, and characterization of polymer brushes and grafted polymers, enabling autonomous experimentation and accelerating applications in various fields.

Contribution

It introduces AI/ML workflows for enhancing microstructure control and high-throughput experimentation in polymer systems, supporting autonomous SDL integration.

Findings

AI/ML accelerates polymer microstructure optimization

High-throughput workflows enable rapid screening

Potential for autonomous synthesis and characterization

Abstract

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.