Correlation between σ-Profile Characteristics and Infinite Dilution Activity Coefficients of Choline Chloride-Urea DES: Experimental Determination and Machine Learning Interpretation.

This study integrates inverse gas chromatography (IGC) experiments with machine learning (ML) to systematically investigate the thermodynamic properties of choline chloride (ChCl)-urea (1:2) deep eutectic solvent (DES) and its interaction mechanisms with organic solvents. IGC measurements determined the infinite dilution activity coefficients (γ₁₂^∞) and related thermodynamic parameters for 46 representative organic solvents within the temperature range of 303.15-343.15 K. Results revealed the hierarchy of solute-DES interaction strength: hydrocarbons (increasing with chain length) > alkenes > ethers > aromatics > ketones > esters > alcohols (weakest due to hydrogen bonding). To enhance γ₁₂^∞ prediction accuracy, a novel approach fused the quantized σ-profile partitioning descriptors of the DES with temperature as input features, constructing four ML models. Compared to the significant deviation of the COSMO-SAC model prediction (R² = 0.8224), the Extreme Gradient Boosting (XGBoost) model demonstrated superior performance (test set R² = 0.9979, average absolute relative deviation (AARD) < 20%). Feature importance analysis indicated that σ-profile regions corresponding to weak hydrogen bond acceptor (HBAs) character [S3: -0.0084 ≤ σ ≤ 0 e/Ų] and weak hydrogen bond donor character [S4, 0 ≤ σ ≤ 0.0084 e/Ų] contributed dominantly (42%) to the γ₁₂^∞ prediction. In contrast, the strongly polar region [S5, 0.0084 ≤ σ ≤ 0.02 e/Ų] reduced γ₁₂^∞ by enhancing interactions, confirming the "like dissolves like" principle. This framework enables high-precision γ₁₂^∞ prediction solely from molecular structures (Applicability Domain (AD) covers 93.85% of data), providing an efficient and reliable theoretical tool for DES-based green solvent design and optimization of industrial separation processes, such as benzene/methanol systems.