Wang Yiming, Fang Yue, Zhou Haifan, Gao Hanyu
Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
Molecules. 2024 Oct 3;29(19):4694. doi: 10.3390/molecules29194694.
The propagation rate coefficient () 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 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 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 monomers, which indicates that the model exhibits strong generalization capabilities and provides reliable and robust predictions. In addition, this model can accurately predict the influence of the ester side chain length of (meth)acrylates on , aligning well with established scientific knowledge. This approach offers a straightforward and practical model for other researchers to rapidly obtain accurate values by employing monomer structural information. The model is sufficiently general to apply to a wide range of (meth)acrylate and butadiene FRP monomers, thereby supporting kinetic modeling of polymerization reactions.
链增长速率系数（）是自由基聚合（FRP）中最关键的动力学参数之一，因为它直接决定聚合速率和所得的分子量分布。FRP中的通常可通过实验测量或量子化学计算获得，这两种方法都可能耗时且资源密集。在此，我们仅基于FRP中涉及的单体的结构特征开发了一种机器学习模型，利用分子嵌入和套索回归算法更高效、准确地预测。结果表明，该模型对四种新单体的预测平均绝对百分比误差（MAPE）仅为5.49%，这表明该模型具有很强的泛化能力，并提供可靠且稳健的预测。此外，该模型可以准确预测（甲基）丙烯酸酯的酯侧链长度对的影响，与已有的科学知识高度吻合。这种方法为其他研究人员提供了一个直接实用的模型，通过利用单体结构信息快速获得准确的 值。该模型具有足够的通用性，可应用于广泛的（甲基）丙烯酸酯和丁二烯FRP单体，从而支持聚合反应的动力学建模。
