Liu Qinrui, Forrester Michael F, Dileep Dhananjay, Subbiah Aadhi, Garg Vivek, Finley Demetrius, Cochran Eric W, Kraus George A, Broderick Scott R
Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USA.
Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011, USA.
Int J Mol Sci. 2025 Mar 18;26(6):2743. doi: 10.3390/ijms26062743.
This paper develops a machine learning methodology for the rapid and robust prediction of the glass transition temperature (Tg) for polymers for the targeted application of sustainable high-temperature polymers. The machine learning framework combines multiple techniques to develop a feature set encompassing all relative aspects of polymer chemistry, to extract and explain correlations between features and Tg, and to develop and apply a high-throughput predictive model. In this work, we identify aspects of the chemistry that most impact Tg, including a parameter related to rotational degrees of freedom and a backbone index based on a steric hindrance parameter. Building on this scientific understanding, models are developed on different types of data to ensure robustness, and experimental validation is obtained through the testing of new polymer chemistry with remarkable Tg. The ability of our model to predict Tg shows that the relevant information is contained within the topological descriptors, while the requirement of non-linear manifold transformation of the data also shows that the relationships are complex and cannot be captured through traditional regression approaches. Building on the scientific understanding obtained from the correlation analyses, coupled with the model performance, it is shown that the rigidity and interaction dynamics of the polymer structure are key to tuning for achieving targeted performance. This work has implications for future rapid optimization of chemistries.
本文开发了一种机器学习方法，用于快速、稳健地预测聚合物的玻璃化转变温度（Tg），以用于可持续高温聚合物的目标应用。该机器学习框架结合了多种技术，以开发一个包含聚合物化学所有相关方面的特征集，提取并解释特征与Tg之间的相关性，并开发和应用高通量预测模型。在这项工作中，我们确定了对Tg影响最大的化学方面，包括与旋转自由度相关的参数和基于空间位阻参数的主链指数。基于这种科学认识，在不同类型的数据上开发模型以确保稳健性，并通过测试具有显著Tg的新型聚合物化学来获得实验验证。我们的模型预测Tg的能力表明，相关信息包含在拓扑描述符中，而数据的非线性流形变换的要求也表明这些关系很复杂，无法通过传统回归方法捕捉。基于从相关性分析中获得的科学认识，结合模型性能，结果表明聚合物结构的刚性和相互作用动力学是实现目标性能调控的关键。这项工作对未来化学的快速优化具有重要意义。
