The application of machine learning in 3D/4D printed stimuli-responsive hydrogels

The integration of machine learning (ML) in materials fabrication has seen significant advancements in recent scientific innovations, particularly in the realm of 3D/4D printing. ML algorithms are crucial in optimizing the selection, design, functionalization, and high-throughput manufacturing of materials. Meanwhile, 3D/4D printing with responsive material components has increased the vast design flexibility for printed hydrogel composite materials with stimuli responsiveness. This review focuses on the significant developments in using ML in 3D/4D printing to create hydrogel composites that respond to stimuli. It discusses the molecular designs, theoretical calculations, and simulations underpinning these materials and explores the prospects of such technologies and materials. This innovative technological advancement will offer new design and fabrication opportunities in biosensors, mechatronics, flexible electronics, wearable devices, and intelligent biomedical devices. It also provides advantages such as rapid prototyping, cost-effectiveness, and minimal material wastage.