Trainable Hydrogels: Mechanistic Principles, Training Strategies, and Frontier Applications

Hydrogels represent a versatile family of soft materials, yet their adaptive behavior and programmable functionality face substantial limitations under complex operational environments. Drawing upon the biological principle whereby skeletal muscles undergo structural remodeling and functional enhancement under sustained mechanical loading, trainable hydrogels have emerged as a cutting-edge class of biomimetic smart materials. Such materials undergo dynamic structural evolution in response to external stimuli and retain long-lived memory of mechanical, responsive, and functional traits. This review systematically catalogues the core training methodologies for trainable hydrogels, encompassing mechanical, thermal, optical, electrical, and chemical regimes (including pH, ionic, and solvent-exchange treatments), alongside multi-modal synergistic training protocols and post-training stabilization strategies. Key advances in performance modulation, fundamental structure-property correlations and standardized characterization workflows are elaborated, with a comprehensive overview of their deployment across soft robotics, biomedicine, wearable health monitoring, human-machine interfaces and additive manufacturing. Whilst significant progress has been achieved within this research field, critical challenges persist, including scalable manufacturing, unresolved multi-stimulus coupling mechanisms and inadequate long-term operational stability. Prospective research avenues center on precise multi-physics modulation, in situ characterization, machine learning-assisted rational material design and unified integrated printing-training fabrication workflows, which will underpin the development of next-generation adaptive intelligent hydrogel platforms.