Large-deformation functional materials: mechanistic fundamentals, processing strategies, applications, and machine learning-assisted design

Abstract

Materials capable of accommodating large deformation while maintaining recoverability or functional stability are attracting increasing attention in aerospace structures, biomedical devices, soft robotics, and wearable electronics. Among these materials, shape memory alloys (SMAs), shape memory polymers and their composites (SMPs/SMPCs), liquid crystal elastomers (LCEs), and conductive polymer composites (CPCs) exhibit distinct large-deformation responses arising from different physical mechanisms. SMAs achieve large recoverable deformation through reversible martensitic transformation, whereas SMPs/SMPCs rely on stimulus-responsive polymer networks for shape fixation and recovery. LCEs realize programmable deformation through the coupling between liquid-crystalline ordering and elastic polymer networks, while CPCs accommodate large deformation through flexible polymer matrices, where the dynamic reconstruction of conductive networks during deformation enables electromechanical coupling and multifunctional sensing responses. Building on these distinctions, this review examines the material characteristics, response mechanisms, preparation strategies, and engineering applications of these systems. The applications of machine learning in material performance prediction, structural optimization, process regulation, and intelligent sensing are also reviewed. Finally, the key challenges and future perspectives are discussed to provide insights into the further development of large-deformation functional materials.

Graphical abstract

Keywords

shape memory alloys / shape memory polymers / liquid crystal elastomers / conductive polymer composites / machine learning

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