Random Forest QSPR Models With Quantum Chemical Descriptors for Extrapolative Prediction of the Flory‐Huggins χ Parameter

This study rigorously evaluates QSPR models for predicting the polymer–solvent Flory–Huggins χ parameter using strict group‐based splitting. Two random forest models were developed from 2474 polymer–solvent pairs and 34 quantum‐chemical descriptors. RF Model I was trained on 1981 χ values (11 polymers) and tested on 493 χ values (8 new polymers). RF Model II was trained on 1897 χ values (69 solvents) and tested on 577 χ values (19 new solvents). Both models showed robust predictive power despite challenging extrapolations: Model I achieved R 2 = 0.847 (RMSE = 0.249); Model II achieved R 2 = 0.943 (RMSE = 0.292). Minor systematic bias was observed, but strong correlation and ranking ability for novel chemistries were retained. This work highlights the importance of rigorous validation and provides a reliable QSPR framework for in silico screening of polymer–solvent compatibility.