biostudies-literatureWang YHong Kong PhD Fellowship SchemeHong Kong Research Grants Council Early Career SchemeHong Kong University of Science and Technology4694https://www.ebi.ac.uk/biostudies/studies/S-EPMC11477705biostudies-literatureUnknown29(19)The propagation rate coefficient (<i>k</i>_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 <i>k</i>_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 <i>k</i>_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 monomeMolecules (Basel, Switzerland)A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization.PMC1147770526214522Start-Up FundPF2278203Fang YGao HWang YZhou HfalseA Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization.The propagation rate coefficient (<i>k</i>_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 <i>k</i>_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 <i>k</i>_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 monome2024-01-01T00:00:00Z2024 Oct2025-04-04T13:54:41.846Z2025-04-04T13:54:41.846ZS-EPMC114777053940762210.3390/molecules29194694