Data-driven de novo design of super-adhesive hydrogels

Abstract Data-driven methodologies have revolutionized the discovery and prediction of new hard materials, such as crystal structures and high-entropy alloys1-5. However, their application to soft materials remains challenging due to the inherent complexity of their structure–property relationships6-8. Here, we present a comprehensive data-driven approach that integrates data mining, experimentation, and machine learning to develop high-performance adhesive hydrogels from scratch, tailored for demanding underwater environments. By leveraging protein databases, we devised a descriptor strategy to statistically replicate protein sequence patterns via ideal random copolymerization, enabling targeted hydrogel design and dataset construction. Using machine learning, we optimized hydrogel formulations even with a small dataset, achieving unprecedented adhesive performance. These super-adhesive hydrogels demonstrate immense potential across diverse applications, from biomedical engineering to deep-sea exploration, marking a significant advancement in the data-driven innovation for soft materials.