Data-driven de novo design of super-adhesive hydrogels.

ABSTRACT: Data-driven methodologies have transformed the discovery and prediction of hard materials with well-defined atomic structures by leveraging standardized datasets, enabling accurate property predictions and facilitating efficient exploration of design spaces^1-3. However, their application to soft materials remains challenging because of complex, multiscale structure-property relationships^4-6. Here we present a data-driven approach that integrates data mining, experimentation and machine learning to design high-performance adhesive hydrogels from scratch, tailored for demanding underwater environments. By leveraging protein databases, we developed a descriptor strategy to statistically replicate protein sequence patterns in polymer strands by ideal random copolymerizati

SUBMITTER: Liao H 

PROVIDER: S-EPMC12328221 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature