From material-derived to artificial intelligence-empowered intelligent hydrogels

Abstract

Material-derived intelligent hydrogels have demonstrated remarkable application potential in biomedicine, flexible electronics, food, and other fields due to their dynamic response capabilities to external environmental factors. In recent years, machine learning (ML)-empowered intelligent hydrogels have promoted the transformation of material-derived intelligent hydrogels into intelligent systems with “intelligent design-perception-decision” functions. However, previous studies have focused on the intelligent response mechanisms and regulatory optimization of material-derived hydrogels, lacking a systematic review of ML-empowered intelligent hydrogel systems. This review combines material-derived intelligent hydrogels with ML systematically reviews their regulation strategies ML-empowered mechanisms and practical applications and points out the main challenges and future research directions. Starting from the material derivation mechanism, it elucidates how the mechanism supports basic intelligent responses. Then, macroscopic regulation methods for shape changes are discussed, including swelling/shrinking, bending, rigidity, and self-healing capabilities. Additionally, advanced ML-empowered intelligent hydrogel systems are highlighted, discussing ML in intelligent hydrogel systems, data-driven material design, and intelligent analysis of multi-dimensional response behaviors. Finally, this paper summarizes the innovative applications of ML-empowered intelligent systems in multiple fields and proposes the challenges and prospects they face.