Predicting Synergistic and Antagonistic Micellization Effects in Surfactant Blends for Optimized Formulation Design

Within Rubingh’s Regular Solution Approximation (RST), these interactions are described by the parameter β, which is typically obtained by fitting experimental mixture data, restricting its applicability in large-scale formulation design.

In this work, a computational hybrid approach combining machine learning with thermodynamic modeling is introduced to enable end-to-end prediction and optimization of surfactant mixtures. A dataset of interaction parameters derived from experimental binary surfactant mixtures was constructed to train predictive models for β, together with models for pure-component properties. The predicted, composition-dependent β values are incorporated into the RST framework to estimate mixture behavior without requiring experimental mixture data, while treating the resulting activity coefficients as effective quantities.