A Predictive machine-learning model for propagation rate coefficients in radical polymerization?

Polymer Chemistry Pub Date: 2023-03-10 DOI: 10.1039/D2PY01531E

Abstract

Using a ridge regression, the propagation rate coefficients for radical polymerization are correlated with basic molecular properties. These are either available from literature, or from simple and non-time-consuming calculations. Parameters under consideration are molecular weights, boiling points, and dipole moments. The model is applicable to both acrylates and methacrylates with linear and branched structures, as well as monomers that are known to be influenced strongly by H-bonding, allowing to fit all data in a single approach. The model also successfully correlates monomers such as styrene and acrylonitrile. Absolute rate coefficients, as well as Arrhenius activation parameters can be described with good accuracy. With the presented model it is thus possible to describe practically all monomers for which kinetic data is available simultaneously and to carry out predictions for monomers for which no experimental data exist.

Graphical abstract: A Predictive machine-learning model for propagation rate coefficients in radical polymerization
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