Accelerating Polyester Intelligence: Machine-Learning-Assisted Prediction of Glass Transition Temperature and Virtual Molecules Screening

Rapid development of the economy and society has resulted in a need for polyesters that are tailored to diverse performance requirements. Unfortunately, the innovation of polyester materials is mainly dependent on experience and intuitive guidance. Herein, we propose various interpretable quantitative-structure–property relationship (QSPR) models based on machine-learning-assisted approaches, which can accurately predict polyesters’ glass transition temperatures (Tg) and facilitate the exploration of novel polyesters. Initially, 695 polyesters with Tg values are collected to establish multiple QSPR models using three different algorithms, which undergo both internal and external validation. The relative coefficient (R2) values of the best deep neural network (DNN) model on the training set and testing set reach 0.9588 and 0.9314, respectively, which is among the better levels in related studies. The use of Morgan fingerprint with frequency (MFF) descriptors and associated Shapley Additive Explanations analysis does reveal a couple of interesting physical trends associated with variation of Tg with the substructure beyond what was reported before. To better widen the chemical space of the existing polyester material family, a virtual polyester library is constructed using a retrosynthetic strategy. Furthermore, this workflow identifies 20 novel polyesters with low synthetic complexity by high-throughput screening and validates these polyesters through molecular dynamics simulations, which show an average absolute error of 9.42 °C between the model-predicted and MD-simulated values. Machine-learning-assisted approach not only improves the efficiency of polyester material discovery but also provides a promising perspective for understanding the thermal properties of polyesters from a microscopic chemical structural viewpoint.经济社会的快速发展，催生了适配多样化性能需求的聚酯材料研发需求。遗憾的是，当前聚酯材料的创新研发仍主要依赖经验与直觉引导。为此，本文提出多种基于机器学习辅助方法的可解释性定量构效关系（quantitative-structure–property relationship, QSPR）模型，可精准预测聚酯的玻璃化转变温度（glass transition temperature, Tg），助力新型聚酯材料的开发探索。本文首先收集了695组带有Tg数据的聚酯样本，采用三种不同算法构建多类QSPR模型，并对模型进行内部与外部验证。最优深度神经网络（deep neural network, DNN）模型在训练集与测试集上的决定系数（R²）分别可达0.9588与0.9314，达到相关研究中的较高水平。结合带频率的摩根指纹（Morgan Fingerprint with Frequency, MFF）描述符与沙普利加性解释（Shapley Additive Explanations）分析，本文揭示了此前未被报道的、与Tg随子结构变化相关的若干有趣物理趋势。为进一步拓展现有聚酯材料的化学空间，本文采用逆合成策略构建了虚拟聚酯库。此外，本研究通过高通量筛选从虚拟库中识别出20种合成复杂度较低的新型聚酯，并借助分子动力学（molecular dynamics, MD）模拟对其进行验证，模型预测值与模拟值的平均绝对误差仅为9.42 ℃。机器学习辅助方法不仅提升了聚酯材料的研发效率，还为从微观化学结构视角理解聚酯的热性能提供了极具前景的研究思路。