Stimuli-Responsive Smart Polymer: A Precise Era with Artificial Intelligence and Machine Learning

Abstract Artificial intelligence (AI) and machine learning (ML)-based solutions are gaining popularity in the field of accelerated material design and development. In this review, we assess AI/ML-enabled advances in stimuli-responsive polymer informatics, with a focus on developing application-specific, practical polymeric materials. We look at exemplary design techniques in several critical and developing application areas, including materials design for energy production, storage, and conservation, as well as enabling a sustainable economy based on recyclable and/or biodegradable polymers. Early demonstrations demonstrated that the ML can accurately predict critical polymer characteristics and phase behavior, accelerating the discovery of high-performance compositions and optimizing processing conditions for stimuli-responsive hydrogels, thermosets, and 3D/4D printed smart structures. Integrating AI-assisted modeling with real-time sensing in smart polymers allows for closed-loop control over actuation, shape morphing, drug release, and solubility/swelling changes in complicated, time-varying environments. The combination of stimuli-responsive polymeric smart materials with AI/ML-driven design and control enables programmable, application-specific smart materials that correspond with precision chemistry’s goal.