Data-Driven Inverse Design of Ionically Conductive Hydrogel Formulations via Generative and Graph-Based Learning
Ionically conductive hydrogels are promising materials for flexible sensors, electronic skin, and wearable electronics owing to their compliance, stretchability, and ionic conductivity. However, the formulation design of multicomponent hydrogels still largely relies on trial-and-error experiments, making it inefficient to simultaneously optimize multiple electromechanical properties. Here, we propose a data-driven inverse design strategy for ionically conductive hydrogel formulations. Based on 233 experimentally measured formulation–property samples, a conditional formulation generation model based on a conditional Wasserstein generative adversarial network with gradient penalty was first developed to generate candidate formulations under target property constraints. A component-aware hydrogel graph Transformer model, HydroGT, was then constructed to represent each formulation as a component graph for multiproperty prediction and candidate screening. HydroGT achieved an average R2 of 90.44% and an average NRMSE of 6.76% for five-property prediction, outperforming conventional regression models. Experimental validation showed that the formulation obtained by the complete generation–screening framework exhibited lower relative errors across all five target properties than the formulation generated without screening. This work provides an efficient data-driven strategy for the target-oriented design of multicomponent ionically conductive hydrogels.