Machine Learning-Based Prediction of Phase Equilibria and Interfacial Properties of Multicomponent CO₂ Mixtures with...

Impurities in captured CO₂ broaden the two-phase envelope, decrease the interfacial tension (IFT), and affect safe dense-phase transport. These properties are difficult to measure and expensive to compute using molecular simulations. We present the first systematic machine learning (ML) surrogate framework for phase equilibria and interfacial properties of impure CO₂, trained on Perturbed-Chain Polar Statistical Associating Fluid Theory (PCP-SAFT) equation of state (EoS) + cDFT data. Within this framework, the hyperparameter-optimized TabPFN most accurately predicts P _bubble, P _dew, IFT, and interfacial thickness. For IFT, we combined the Winterfeld-Scriven-Davis correlation with an ML-based residual correction. The hybrid model with stochastic variational Gaussian process regression reduces the root mean squared error (RMSE) of IFT to 0.02 mN m^-1. Symbolic regression additionally provides an interpretable expression for this correction. For sampling CO₂-rich compositions, active learning is the most data-efficient strategy. Trained surrogates predict each state point in ca. 1-5 ms, compared to ca. 0.18-1.8 s for cDFT, enabling rapid property estimation for computational fluid dynamics (CFD), inverse design, and the planning of simulations and experiments.