A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization.

ABSTRACT: The propagation rate coefficient (k_p) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The k_p in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict k_p more efficiently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monome

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11477705 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature