The interfacial tension between organic solvents and water at different temperatures is predicted using coarse-grained, mesoscopic Dissipative Particle Dynamics (DPD) simulations. The temperature effect of the DPD repulsive interaction parameters, aij, for the different components is calculated from the dependence of the Flory-Huggins χ parameter on temperature, by means of the solubility parameters. Atomistic simulations were carried out for the calculation of the solubility parameters for different organic compounds at different temperatures in order to estimate χ and then the aij coefficients. We validate this parametrization through the study of the interfacial tension in a mixture of benzene and water, and cyclohexane and water, varying the temperature. The predictions of our simulations are found to be in good agreement with experimental data taken from the literature, and show that the use of the solubility parameter at different temperatures to obtain the repulsive DPD parameters is a good alternative to introduce the effect of temperature in these systems.

The study of the modification of interfacial properties between an organic solvent and aqueous electrolyte solutions is presented by using electrostatic dissipative particle dynamics (DPD) simulations. In this article, the parametrization for the DPD repulsive parameters aij for the electrolyte components is calculated considering the dependence of the Flory-Huggins χ parameter on the concentration and the kind of electrolyte added, by means of the activity coefficients. In turn, experimental data were used to obtain the activity coefficients of the electrolytes as a function of their concentration in order to estimate the χ parameters and then the aij coefficients. We validate this parametrization through the study of the interfacial tension in a mixture of n-dodecane and water, varying the concentration of different inorganic salts (NaCl, KBr, Na2SO4, and UO2Cl2). The case of HCl in the mixture n-dodecane/water was also analyzed and the results presented. Our simulations reproduce the experimental data in good agreement with previous work, showing that the use of activity coefficients to obtain the repulsive DPD parameters aij as a function of concentration is a good alternative for these kinds of systems.

Rational design of high-performance viscosifying polymers is critical for enhancing supercritical CO₂ flooding efficiency in enhanced oil recovery (EOR). Traditional experimental and simulation approaches are limited in exploring the vast design space of polymer architecture, flexibility, and intermolecular interactions. This work presents an integrated machine learning (ML) and mesoscopic simulation framework using Dissipative Particle Dynamics (DPD) to accelerate the development of tailored polymeric thickeners. We systematically investigate synergistic effects of linear and branched polymer blends on solvent viscosity under Poiseuille flow, representative of flow in micro-fractures and pore throats. Key molecular descriptors are varied to generate a comprehensive rheological database. This data trains a deep neural network (DNN) surrogate model linking molecular parameters to macroscopic viscosity. The DNN is coupled with gradient ascent optimization for inverse design, enabling rapid virtual screening of thousands of formulations. A focused case study demonstrates that the star-like architectures with associative cores and semi-flexible backbones outperform linear analogs for supercritical CO₂ viscosity enhancement. The optimal candidate—a four-arm star polymer with linear side chains—was validated by DPD simulation. This multiscale “simulation-to-surrogate” methodology bridges molecular design with continuum-scale flow behavior, offering a transformative tool for formulating cost-effective, efficient, and sustainable next-generation EOR chemicals.