A Transferable Four-Tier Decision Architecture for Gas Solubility Prediction in Ionic Liquids
We develop a transferable, physics-inspired feature engineering framework that encodes knowledge of solvation thermodynamics, electrostatic interactions, free-volume effects, and acid–base chemistry directly into the input representation, ensuring that the model learns causal physical relationships rather than data s...