Optimizing oil detachment from silica surfaces using gemini surfactants and functionalized silica nanoparticles: a combined molecular dynamics and machine learning approach

, XGBoost and AdaBoost yielded the best correlation with the observed data. However, for the complex oil model, ridge regression and support vector regression (SVR) outperformed other ML models in SNP prediction, pointing to the existence of a simpler correlation between the descriptors and the output variable. With these findings, the study attempts to streamline the data-driven design of chemical injectants for enhanced oil recovery purposes.