Extended Data Fig. 6: Properties of top-performing hydrogels from machine learning and data mining approaches.

(a) Angular frequency dependence of storage modulus (G′) and loss modulus (G″) for the top-performing machine learning-driven gels (R1-max, R2-max, R3-max) and the top-performing data mining-driven gel (G-max) in DMSO. (b) Angular frequency dependence of G′ and G″ for the four hydrogels equilibrated in normal saline (0.154 M NaCl). (c) Volume swelling ratio (Q) of the four hydrogels equilibrated in normal saline relative to their as-prepared state in DMSO. (d) Pure shear stress-stretch ratio curves for the R1-max hydrogel (equilibrated in normal saline) with and without a notch, measured at a stretch rate of 100 mm min⁻¹. The notched sample exhibited crack propagation at a critical stretch ratio (λ_c) of 3.4. The fracture energy (Γ) estimated from the pure-shear test is shown. Experimental details are provided in the Supplementary Materials. Error bars represent the standard deviation of N = 3 measurements.