A Machine Learning Model for Copolymer Radical Reactivity Ratio Predictions with Frontier-Orbital Insights

Abstract

Accurate predictions of reactivity ratios (RRs) are crucial for understanding and controlling copolymerization kinetics and the resulting copolymer microstructure. While various methods have been proposed for RR prediction, prior efforts have been limited by a lack of data accessibility, model interpretability, and out-of-distribution performance on new chemical spaces. We address these challenges by assembling a data set of copolymer RRs extracted from the experimental literature and then developing a machine learning model that demonstrates robustness in predicting RRs for diverse monomers and radical chemistries. The Shapley additive explanations (SHAP) analysis of the machine learning model reveals the significant role of frontier molecular orbital (FMO) interactions, corroborating earlier RR prediction models emphasizing the bipolar reactivity of radicals in copolymerization. Importantly, this machine learning model leads to an intuitive argument based on the relative chemical potential and chemical hardness of comonomers that enables predictions of copolymerization regimes based on simple density functional theory calculations.