Machine Learning-Based Acid-Hydrocarbon Interface Prediction and Ionic Liquids Design

Ionic liquids, with their green chemistry attributes and tunable properties, have garnered significant interest as potential additives in sulfuric acid-catalyzed C4 alkylation. Given the vast combinatorial possibilities of anion-cation pairs, traditional experimental screening methods are inefficient in exploring extensive chemical spaces, thus falling short. To address this challenge, we employed machine learning techniques—most notably the random forest (RF) algorithm—to establish correlations between the structural features of ionic liquids and their enhanced acid-hydrocarbon interfacial properties. This approach enabled the development of predictive models based on diverse descriptors, advancing our understanding and facilitating the selection of ionic liquids for specific applications.Furthermore, to streamline the rational design of novel ionic liquid combinations, we utilized continuous and data-driven molecular descriptors (CDDD) derived from the SMILES codes of the compounds. These descriptors were fed into the Success-History based Adaptive Differential Evolution (SHADE) algorithm, which efficiently navigates and decodes the potential space to identify promising candidates. Substructure constraints were also integrated to ensure the rationality and feasibility of the generated structures.Focusing on key parameters such as interfacial thickness (δ_w) and tension (γ) in C4 alkylation, the constructed predictive models achieved a determination coefficient (R²) of 0.952 for interfacial thickness and 0.901 for interfacial tension on the test set, indicating high predictive accuracy. Additionally, through optimization via the SHADE algorithm, 328 ionic liquid combinations meeting the requirements for interfacial thickness and tension were successfully generated, significantly expanding the feasible range of qualifying ionic liquid combinations. This work not only enhances the capability to predict acid-hydrocarbon interfacial properties but also provides novel methodologies and insights for the rational design of ionic liquids.