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 an extension of transformer-based language models to polymerization for both reaction and retrosynthesis tasks. To that end, we have curated a polymerization dataset for vinyl polymers covering reactions and retrosynthesis for representative homo-polymers and co-polymers. Overall, we obtain a forward model Top-4 accuracy of 80% and a backward model Top-4 accuracy of 60%. We further analyze the model performance with representative polymerization examples and evaluate its prediction quality from a materials science perspective. To enable validation and reuse, we have made our models and data available in public repositories.

聚合物是碳捕获和能源存储等广泛可持续应用的候选材料。然而，计算聚合物发现缺乏反应途径的自动分析和通过逆合成的稳定性评估。在这里，我们报告了基于变压器的语言模型对反应和逆合成任务聚合的扩展。为此，我们策划了乙烯基聚合物的聚合数据集，涵盖代表性均聚物和共聚物的反应和逆合成。总体而言，我们获得了 80% 的前向模型 Top-4 准确度和 60% 的后向模型 Top-4 准确度。我们通过代表性聚合示例进一步分析模型性能，并从材料科学的角度评估其预测质量。为了实现验证和重用，我们在公共存储库中提供了模型和数据。