Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

ABSTRACT The extracellular matrix (ECM) exhibits tissue‐specific viscoelasticity with a unique combination of elasticity and viscosity. Hydrogels with independently controlled elastic modulus and stress relaxation have been developed. Yet, time‐ and labor‐efficient identification of formulations used for the preparation of multicomponent hydrogels recapitulating the mechanical properties of a specific tissue remains a challenge. Conventional bottom‐up screening of hydrogel formulations is resource‐intensive, especially for navigating multicomponent hydrogels. Here, we present an active learning framework based on multi‐objective Bayesian optimization to accelerate the discovery of biomimetic fibrous hydrogels replicating the elastic and viscous properties of the ECM of healthy and diseased tissues. In the data‐driven approach, we iteratively navigated the multidimensional chemical space, while simultaneously optimizing hydrogel's conflicting mechanical properties and mapping the boundaries of material feasibility using sparse experimental feedback. The experimental data were used to train a digital twin model and unveil the interplay between the fabrication conditions and hydrogel properties. The decoupled effects of the elasticity and viscosity were established for cell activation, proliferation, and morphogenesis. This work shows the potential of using the data‐driven approach, leveraging even sparse experimental data to engineer hydrogels and optimize diverse biomaterials for tissue engineering and regenerative medicine.