State-Constrained SHAP Distills Polymer Property Models into Transferable Structural Rules for Fixed-Interval...

Polymer informatics has accelerated data-driven prediction of polymer properties, yet accurate prediction does not directly enable discovery. Here, we introduce state-constrained SHAP (SC-SHAP), an attribution-to-rule distillation framework for fixed-interval polymer screening. Using XGBoost as the teacher, SC-SHAP reorganizes SHAP and SHAP-interaction attributions on chemically interpretable MACCS fingerprints into support-qualified structural states defined by required-present and required-absent substructures. These states provide auditable property ordering, executable structural conditions, and candidate-level rule traces, while the learned library is reused across target windows with interpolation-only boundary calibration. Evaluation on experimental PoLyInfo datasets for glass-transition temperature (Tg), tensile stress at break (TSS), and electrical conductivity (EC) yields Spearman correlations between state scores and mean measured properties of 0.908 ± 0.008, 0.817 ± 0.069, and 0.835 ± 0.025, respectively. Benchmarks include direct XGBoost, MACCS-GCN, MACCS-Transformer, and rule-based screening. In the fixed-interval screening benchmark, SC-SHAP achieves higher mean scores than direct XGBoost, MACCS-GCN, MACCS-Transformer, and WRAcc on the smaller TSS and EC datasets. Multiple seeds and alternative structural splits further assess transfer to structurally distinct polymers. By unifying nonlinear knowledge distillation, chemically explicit rules, and cross-window reuse, SC-SHAP provides an interpretable screening framework spanning thermal, mechanical, and electrical polymer properties.