Elucidation of Structure–Reactivity Trends in Free Radical Copolymerization Reactivity Ratios Using Data Science Methods

Copolymerization is a powerful tool for tuning the properties of synthetic polymers. In chain-growth polymerization, copolymer microstructure is controlled by the relative reactivity of comonomers, quantified by copolymer reactivity ratios. Yet, the complex macromolecular reactivity captured by reactivity ratios precludes the prediction of the resultant microstructures by intuition, and the established theory to understand reactivity ratios (i.e., Q- e scheme) has not been updated substantially since the development of quantum mechanics. Here, we combine modern computation, data science, and experimental validation to gain a detailed structure–reactivity understanding of the electronic, energetic, and steric parameters that govern comonomer reactivity. Using >450 reactivity ratios identified from primary literature sources, a combination of parametrization from density functional theory and data science methods identified discrete structure–reactivity relationships in free radical copolymerization. We demonstrate that the electronics of the radical species and the sterics of the monomers, neither of which were captured in the previous Q- e scheme, are pivotal to understanding the copolymerization reactivity. Experimentally measured reactivity ratios of a previously unstudied monomer combination validated the models, demonstrating the potential predictive power of this work to understand the comonomer sequence a priori . This work provides a framework to understand polymer microstructure from first-principles by relating specific and interpretable attributes of monomer structure to comonomer reactivity.