Predicting polymerization reactions via transfer learning using chemical language models

Polymers are candidate materials for a wide range of sustainability applications such as carbon capture and energy storage. However, computational polymer discovery lacks automated analysis of reaction pathways and stability assessment through retro-synthesis. Here, we report the first extension of transformer-based language models to polymerization reactions for both forward and retrosynthesis tasks. We curated a polymerization dataset for vinyl polymers covering reactions and retrosynthesis for representative homo-polymers and co-polymers. Overall, we report a forward model accuracy of 80% and a backward model accuracy of 60%. We further analyse the model performance on a set of case studies by providing polymerization and retro-synthesis examples and evaluating the model’s predictions quality from a materials science perspective.

聚合物作为众多可持续应用领域的候选材料，如碳捕获与储能等，备受瞩目。然而，在计算聚合物发现领域，缺乏对反应途径的自动化分析与通过逆合成法进行的稳定性评估。在此，我们报道了基于Transformer语言模型在聚合反应中首次应用于正向与逆合成任务的拓展。我们精心构建了一个涵盖代表性均聚物与共聚物的反应与逆合成任务的乙烯聚合物聚合数据集。总体而言，我们报告了正向模型准确率为80%，逆向模型准确率为60%。此外，我们通过对一系列案例研究进行深入分析，提供了聚合与逆合成实例，并从材料科学的角度评估了模型的预测质量。