高Tg聚合物的信息学建模：评估加工与化学的作用
Informatics Modeling of High Tg Polymers: Assessing the Role of Processing versus Chemistry
AI总结：
研究聚合物玻璃化转变温度（Tg）预测难题，先扩展基于聚合物拓扑描述符的机器学习模型应用范围，又纳入加工参数，发现多数聚合物Tg主要由化学和结构驱动，少数聚合物预测与实验值有偏差，揭示了相关影响因素。
AI中文摘要：
尽管聚合物性能的基于结构的建模取得了进展，但对于其行为受分子间相互作用和加工条件强烈影响的聚合物，准确预测玻璃化转变温度（Tg）仍然具有挑战性。我们之前开发了一个基于聚合物拓扑描述符的机器学习模型来预测Tg。该模型表现良好，仅基于聚合物的化学和结构，不包含任何加工参数。在这项工作中，我们首先将该模型应用于更大范围的聚合物，其次将加工参数纳入特征集来扩展之前的工作。基于化学的模型对大多数聚合物仍表现出一致的预测性能，表明Tg确实主要由化学和结构驱动，不受加工的强烈影响。然而，几种聚合物的预测Tg值与实验值之间存在偏差。详细分析表明，这些差异与强分子间相互作用和加工相关因素有关，特别是对于通过溶液浇铸和高温退火制备的聚合物。这些结果表明分子拓扑为Tg预测提供了坚实基础；然而，这种方法也筛选出了那些加工条件起重要作用的聚合物类别。
英文摘要：
Despite the advances in structure-based modeling of polymer properties, accurately predicting glass transition temperature (Tg) is still challenging for polymers whose behavior is strongly influenced by intermolecular interactions and processing conditions. We previously developed a machine-learning model based on polymer topological descriptors to predict Tg. The model performed well and was based solely on the chemistry and structure of the polymer without any inclusion of processing parameters. In this work, we have extended that work by first applying that same model to a larger range of polymers and second by integrating processing parameters into the feature set. The chemistry-based model still demonstrates consistent predictive performance for most polymers, indicating that Tg is indeed primarily chemistry and structure driven and not strongly impacted by processing. However, several polymers exhibited deviations between predicted and experimental Tg values. Detailed analysis reveals that these differences are related to strong intermolecular interactions and processing-dependent factors, particularly for polymers prepared by solution casting and high temperature annealing. These results demonstrate that molecular topology provides a strong foundation for Tg prediction; however, this approach also screens out those classes of polymers for with processing conditions play an important role.
