Ejeromedoghene Onome, Kumi Moses, Akor Ephraim, Zhang Zexin
College of Chemistry, Chemical Engineering and Materials Science, Soochow University, 199 Renai Road, 215123 Suzhou, Jiangsu Province, PR China.
Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE), Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, 710072 Xi'an, Shaanxi Province, PR China.
Adv Colloid Interface Sci. 2025 Feb;336:103360. doi: 10.1016/j.cis.2024.103360. Epub 2024 Nov 27.
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.
机器学习（ML）在材料制造中的整合在最近的科学创新中取得了显著进展，特别是在3D/4D打印领域。ML算法对于优化材料的选择、设计、功能化和高通量制造至关重要。同时，具有响应性材料组件的3D/4D打印增加了具有刺激响应性的印刷水凝胶复合材料的巨大设计灵活性。本综述重点关注在3D/4D打印中使用ML来创建对刺激有响应的水凝胶复合材料的重大进展。它讨论了支撑这些材料的分子设计、理论计算和模拟，并探索了此类技术和材料的前景。这一创新技术进步将在生物传感器、机电一体化、柔性电子、可穿戴设备和智能生物医学设备中提供新的设计和制造机会。它还具有快速原型制作、成本效益高和材料浪费最小等优点。
