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 set-specific artifacts. Through a four-step ablation protocol under GroupKFold cross-validation, where generalization is evaluated on entirely unseen ionic liquid (cation + anion)gas compositional triplets, we identify a Pareto-optimal Thermodynamic + Electrostatic model.

Critically, SHAP analysis reveals that the model has autonomously learned a four-tier decision architecture that mirrors established physicochemical hierarchies: a thermodynamic baseline, structure-mediated microtuning, an electrostatic binary switch, and a phase-state classifier. The transferability of this architecture was further evaluated through zero-shot generalization to three entirely unseen gases (C₂H₄, SO₂, and N₂), which span distinct regions of the physicochemical interaction spectrum and extend the evaluation beyond the gas chemistries represented during training.