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.

Experimentally determining interfacial tension (IFT) for compositions relevant to CO₂ transport is challenging. We address this using molecular dynamics (MD) simulations and perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state with classical density functional theory. We compute phase equilibria and interfacial properties of pure CO₂ and CO₂–CH₄, CO₂–Ar, CO₂–N₂, and CO₂–H₂ mixtures at 220–273 K. Both approaches accurately estimate CO₂ phase equilibria and IFTs. For binary mixtures, phase equilibria computed using PC-SAFT agree well with experiments when k_ij ≠ 0. IFTs computed from PC-SAFT depend strongly on k_ij, while MD simulations systematically overpredict IFTs. The IFT decreases with increasing pressure, least pronouncedly for H₂-containing mixtures. Binary mixtures exhibit interfacial enrichment of the light boiling component, decreasing with increasing temperature and pressure. Semiempirical Parachor and Winterfeld–Scriven–Davis models capture IFT–pressure trends with mixture-dependent accuracy. These results improve predictions of metastable limits and provide key insights for fast-transient multiphase CO₂ flow modeling.

Computation of the excess entropy (Formula presented.) from the second-order density expansion of the entropy holds strictly for infinite systems in the limit of small densities. For the reliable and efficient computation of (Formula presented.) it is important to understand finite-size effects. Here, expressions to compute (Formula presented.) and Kirkwood–Buff (KB) integrals by integrating the Radial Distribution Function (RDF) in a finite volume are derived, from which (Formula presented.) and KB integrals in the thermodynamic limit are obtained. The scaling of these integrals with system size is studied. We show that the integrals of (Formula presented.) converge faster than KB integrals. We compute (Formula presented.) from Monte Carlo simulations using the Wang–Ramírez–Dobnikar–Frenkel pair interaction potential by thermodynamic integration and by integration of the RDF. We show that (Formula presented.) computed by integrating the RDF is identical to that of (Formula presented.) computed from thermodynamic integration at low densities, provided the RDF is extrapolated to the thermodynamic limit. At higher densities, differences up to (Formula presented.) are observed.

Experimentally determining thermophysical properties for various compositions commonly found in CO₂ transportation systems is extremely challenging. To overcome this challenge, we performed Monte Carlo (MC) and Molecular Dynamics (MD) simulations of CO₂ rich mixtures to compute thermophysical properties such as densities, thermal expansion coefficients, isothermal compressibilities, heat capacities, Joule-Thomson coefficients, speed of sound, and viscosities at temperatures of (235-313) K and pressures of (20-200) bar. We computed thermophysical properties of pure CO₂ and CO₂ rich mixtures with N₂, Ar, H₂, and CH₄ as impurities of (1-10) mol % and showed good agreement with available Equations of State (EoS). We showed that impurities decrease the values of thermal expansion coefficients, isothermal compressibilities, heat capacities, and Joule-Thomson coefficients in the gas phase, while these values increase in the liquid and supercritical phases. In contrast, impurities increase the value of speed of sound in the gas phase and decrease it in the liquid and supercritical phases. We present an extensive data set of thermophysical properties for CO₂ rich mixtures with various impurities, which will help to design the safe and efficient operation of CO₂ transportation systems.