The complexity and diversity of polymer topologies, or chain architectures, present substantial challenges in predicting and engineering polymer properties. Although machine learning is increasingly used in polymer science, applications to address architecturally complex polymers are nascent. Here, we use a generative machine learning model based on variational autoencoders and data generated from molecular dynamics simulations to design polymer topologies that exhibit target properties. Following the construction of a dataset featuring 1342 polymers with linear, cyclic, branch, comb, star, or dendritic structures, we employ a multi-task learning framework that effectively reconstructs and classifies polymer topologies while predicting their dilute-solution radii of gyration. This framework enables the generation of polymer topologies with target size, which is subsequently validated through molecular simulation. These capabilities are then exploited to contrast rheological properties of topologically distinct polymers with otherwise similar dilute-solution behavior. This research opens avenues for engineering polymers with more intricate and tailored properties with machine learning.

聚合物拓扑或链结构的复杂性和多样性给预测和工程聚合物性能带来了巨大的挑战。尽管机器学习越来越多地应用于聚合物科学，但解决结构复杂的聚合物的应用才刚刚起步。在这里，我们使用基于变分自动编码器和分子动力学模拟生成的数据的生成机器学习模型来设计具有目标特性的聚合物拓扑。在构建了包含 1342 种具有线性、环状、分支、梳状、星形或树枝状结构的聚合物的数据集之后，我们采用了多任务学习框架，可以有效地重建和分类聚合物拓扑，同时预测其稀溶液回转半径。该框架能够生成具有目标尺寸的聚合物拓扑，随后通过分子模拟进行验证。然后利用这些功能来对比拓扑不同的聚合物的流变特性与其他类似的稀溶液行为。这项研究为通过机器学习工程化具有更复杂和定制特性的聚合物开辟了途径。