Machine learning guided discovery of deep eutectic solvents for <scp> NH ₃ </scp> capture: Experimental validation and mechanism

Abstract Deep eutectic solvents (DESs) have considerable potential for NH 3 capture, but traditional solvent screening methods are unable to identify appropriate DES efficiently. One thousand nine hundred fifty‐nine experimental solubility data points for 72 DESs were used to construct and compare multiple machine learning models based on σ‐profile descriptors to predict NH 3 solubility in DESs. CatBoost achieved the best performance ( R 2 = 0.993, RMSE = 0.079). Nested cross‐validation and independent test sets confirmed that the model has good physical consistency and cross‐system generalization. SHAP analysis further quantified the contributions of key features. The final model was then employed to predict the NH 3 solubilities of 1140 DESs, and the highest‐ranked systems were selected for subsequent characterization and absorption experiments. The results of the gas absorption performance experiment are in excellent agreement with the predictions of the model. Finally, quantum chemical calculations were used to clarify the microscopic mechanisms underlying DES formation and NH 3 interaction.