Investigating Hydrogel Structural Parameters in the Flory–Rehner Model Using Bayesian Optimization

ABSTRACT Hydrogels exhibit tunable solvent absorption driven by environmental stimuli such as temperature, pH, and light. While prior work established connections between synthesis processes and swelling behavior of temperature‐responsive hydrogels, the current study bridges the internal structure and the temperature‐responsive swelling behavior. First, we build a mathematical model for the swelling behavior grounded in the Flory–Rehner theory incorporating the mathematical expression of the Flory–Huggins interaction parameter . We inversely determine the structural parameters inherent to Flory–Rehner theory and the constants from the expression of , by using Bayesian optimization applied to experimental swelling data. This data‐driven approach integrates theoretical frameworks with empirical observations, enabling optimization of structural descriptors critical to volume phase transitions. By embedding mathematical modeling within a data‐driven approach, this research provides a foundation for predicting the internal structure of temperature‐responsive hydrogels from observed swelling behavior, as well as elucidates property–structure relationships. This advances the development of a comprehensive processing–structure–property–performance (PSPP) relationship for hydrogels.