The rational design of polyurethanes (PUs) with tailored thermal properties is often impeded by the complex interplay between molecular architecture and microphase-separated morphology. While machine learning (ML) offers a promising method to accelerate materials discovery, existing models often lack mechanistic interpretability. Herein, we develop a robust predictive model for the glass transition temperature (Tg) by benchmarking a suite of ML algorithms against a carefully curated and expanded PU data set. Among the evaluated architectures, ensemble-based methods demonstrate better performance, with the optimized eXtreme Gradient Boosting (XGBoost) model achieving R2 > 0.91 and RMSE ≈ 9.6 K, outperforming Lasso regression, Random Forest and Support-Vector Regression. Beyond accurate prediction, Shapley Additive Explanations (SHAP) methodology is employed to elucidate the mechanistic underpinnings of feature contributions in determining Tg. The SHAP analysis has been shown to quantitatively identify the density of quaternary carbon units within the hard segment (c HS), the hard segment weight fraction (HS wt %) and the molecular volume of the hard segment (HS MV) as the three dominant physicochemical descriptors governing Tg. Furthermore, all of the significant features that were examined can be categorized into three distinct groups, i.e., positive, negative, and dual effect groups, reflecting the competition between intramolecular chain stiffening and the disruption of intermolecular packing and the hydrogen-bond network. Overall, this study establishes concise data-driven design rules for modulating the thermal behavior of PUs, bridging the gap between statistical learning and fundamental polymer physics and enabling more informed accelerated PU formulation.
Qin et al. (Tue,) studied this question.