AI-Driven Intelligent Optimization and Performance Prediction of Hydrogels

The rapid advancement of artificial intelligence (AI) and machine learning (ML) is reshaping scientific discovery through an information-theoretic par-adigm. Modern large-scale models function as entropy-compression engines: they extract low-entropy, transferable representations from high-dimensional and noisy data, reducing epistemic uncertainty in complex physical systems. This perspective motivates growing interest in entropy- oriented and infor-mation-theoretic approaches to model compression, generalization, and ro-bustness. Soft matter materials—particularly multiscale and compositionally diverse hydrogels—naturally constitute a high-entropy design landscape. Traditional intuition-driven exploration struggles to navigate such spaces or capture nonlinear, cross-scale dependencies reminiscent of complex-system behaviors. To address this challenge, we develop an intelligent hydrogel de-sign framework grounded in information theory and multiscale representa-tion learning. Feature engineering acts as an entropy-reduction step, com-pressing raw variables into structured representations that support stable multi-task learning. A multi-objective Bayesian optimization scheme with Gaussian-process surrogates and a q-Expected Hypervolume Improvement (q-EHVI) acquisition function further performs targeted information acquisi-tion, maximizing posterior entropy reduction while efficiently expanding the Pareto front. A self-evolving feedback loop integrates AI prediction with ex-perimental validation, allowing each iteration to reduce system-level uncer-tainty and refine representations across scales. Incorporating entropy-based and chaos-inspired metrics provides additional diagnostics for robustness and sensitivity, aligning the workflow with emerging information-theoretic prin-ciples used in reliable large-model development. Overall, this study estab-lishes an entropy-aware and representation-driven paradigm for the intelli-gent design of soft functional materials, offering a generalizable pathway for accelerating discovery and enhancing extrapolation capability.