Data from: 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.