To overcome the limitations of empirical synthesis and expedite the discovery of new polymers, this work aims to develop a data‐driven strategy for profoundly aiding in the design and screening of novel polyester materials. Initially, we collected 695 polyesters with their associated glass transition temperatures (Tgs) to develop a quantitative structure–property relationship (QSPR) model. The model underwent rigorous validation (i.e., external validation, internal validation, Y‐random, and application domain analysis) to demonstrate its robust predictive capabilities and high stability. Subsequently, by employing an in‐silico retrosynthesis strategy, over 95,000 virtual polyesters were designed, largely expanding the available space for polyester material family. External assessments were performed, highlighting good extrapolation ability of the QSPR model. Furthermore, we experimentally synthesized 10 designed polyesters with predicted Tgs covering a large temperature range from −42.52 to 103.61°C, and characterization results gave an average absolute error of 17.40°C relative to the predicted ones. It is believed that such data‐driven approach can drive future product development of polymer industry.

中文翻译：

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利用数据驱动策略加速发现具有目标玻璃化转变温度的聚酯

为了克服经验合成的局限性并加快新聚合物的发现，这项工作旨在开发一种数据驱动的策略，以深刻地帮助新型聚酯材料的设计和筛选。最初，我们收集了 695 种聚酯及其相关的玻璃化转变温度（时间Gs) 开发定量结构-性质关系（QSPR）模型。该模型经过严格的验证（即外部验证、内部验证、是‐随机和应用领域分析）以展示其强大的预测能力和高稳定性。随后，通过采用计算机逆合成策略，设计了超过 95,000 种虚拟聚酯，极大地扩展了聚酯材料系列的可用空间。进行了外部评估，强调了 QSPR 模型良好的外推能力。此外，我们还通过实验合成了 10 种设计的聚酯，其预测结果如下：时间G覆盖了-42.52至103.61°C的大温度范围，表征结果相对于预测值的平均绝对误差为17.40°C。相信这种数据驱动的方法可以推动聚合物行业未来的产品开发。