Data-efficient methods for determining Flory–Huggins χ parameters in multicomponent polymer formulations

Polymer formulations are essential in diverse applications, including personal care products, coatings, paints, adhesives, and plastic materials. Designing these formulations requires navigating large, complex design spaces, where phase and self-assembly behavior critically impact performance. The Flory-Huggins $\chi$ parameter, which quantifies segmental miscibility, is widely used to parameterize the excess free energy of mixing in formulation models. In this work, we introduce two data-efficient, top-down methods for estimating $\chi$ parameters using the Random Phase Approximation (RPA): (i) Boundary Nonlinear Regression (Boundary-NLR), which fits theoretical spinodal boundaries to experimental phase boundaries, and (ii) Surrogate Model Inverse Parameter Estimation (SMIPE), which uses a Gaussian Process Classifier to fit sparse phase maps via a surrogate model. Both methods allow rapid parameterization of polymer field-theoretic models without the need for additional experiments. We evaluate these approaches on datasets involving polymer–solvent–nonsolvent ternary mixtures and block copolymer–solvent systems, demonstrating their robustness to experimental noise and their relevance for real-world formulation design.