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Eutectic solvents (ESs) have gained significant interest in various chemical processes due to a broad spectrum of attractive properties, whereas their rational design is currently still in its infancy. To bridge this gap, Redlich−Kister (RK) theory and machine learning are linked for the solid−liquid equilibria (SLE) prediction of ES systems, which is thermodynamically the cornerstone for ES design. RK theory with two or three parameters is first evaluated by fitting experimental SLE of an extensive ES database, demonstrating that the two-parameter-based one is sufficiently reliable for eutectic behavior correlation. Three machine learning methods, namely, Random Forest, multiple linear regression, and ElasticNet, are developed for relating the parameters of RK theory to the RDKit descriptors of ES components. The SLE predictions from RK theory parametrized by the developed machine learning models are carefully evaluated and further externally examined on several recently reported ES systems.

n-Butyl acrylate (n-BA), one of the important chemicals in the resin industry, can be synthesized by esterification of acrylic acid (AA) and n-butanol (BuOH). Owing to the thermodynamic immiscibility of n-BA and water, reactive extraction is promising to intensify the esterification with a suitable solvent (e.g., ionic liquid, IL) through in situ shifting of the reaction equilibrium toward n-BA. Herein, a typical Brønsted acidic IL 1-butyl-3-methylimidazolium hydrogen sulfate ([BMIm]­[HSO4]) was employed to intensify the esterification process. The liquid–liquid equilibrium (LLE) of ternary systems {AA or BuOH + n-BA + [BMIm]­[HSO4]} and a quinary system {AA + BuOH + n-BA + water + [BMIm]­[HSO4]} was investigated at atmospheric pressure and temperatures T = 313.15, 333.15, and 353.15 K and T = 333.15, 343.15, and 353.15 K, respectively. According to experimental results, the solute distribution coefficient and solvent selectivity were tested to verify the thermodynamic feasibility of [BMIm]­[HSO4] as a catalyst and an extractant for n-BA formation. In addition, the NRTL model was adopted to correlate the experimental data, and the binary interaction parameters for the esterification system were fitted.