AlFaraj Yasmeen S, Mohapatra Somesh, Shieh Peyton, Husted Keith E L, Ivanoff Douglass G, Lloyd Evan M, Cooper Julian C, Dai Yutong, Singhal Avni P, Moore Jeffrey S, Sottos Nancy R, Gomez-Bombarelli Rafael, Johnson Jeremiah A
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States of America.
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States of America.
ACS Cent Sci. 2023 Sep 14;9(9):1810-1819. doi: 10.1021/acscentsci.3c00502. eCollection 2023 Sep 27.
Thermosets present sustainability challenges that could potentially be addressed through the design of deconstructable variants with tunable properties; however, the combinatorial space of possible thermoset molecular building blocks (e.g., monomers, cross-linkers, and additives) and manufacturing conditions is vast, and predictive knowledge for how combinations of these molecular components translate to bulk thermoset properties is lacking. Data science could overcome these problems, but computational methods are difficult to apply to multicomponent, amorphous, statistical copolymer materials for which little data exist. Here, leveraging a data set with 101 examples, we introduce a closed-loop experimental, machine learning (ML), and virtual screening strategy to enable predictions of the glass transition temperature () of polydicyclopentadiene (pDCPD) thermosets containing cleavable bifunctional silyl ether (BSE) comonomers and/or cross-linkers with varied compositions and loadings. Molecular features and formulation variables are used as model inputs, and uncertainty is quantified through model ensembling, which together with heavy regularization helps to avoid overfitting and ultimately achieves predictions within <15 °C for thermosets with compositionally diverse BSEs. This work offers a path to predicting the properties of thermosets based on their molecular building blocks, which may accelerate the discovery of promising plastics, rubbers, and composites with improved functionality and controlled deconstructability.
热固性材料存在可持续性挑战，而通过设计具有可调性能的可解构变体可能解决这些挑战；然而，可能的热固性分子构建块（如单体、交联剂和添加剂）以及制造条件的组合空间非常大，并且缺乏关于这些分子组分的组合如何转化为块状热固性材料性能的预测知识。数据科学可以克服这些问题，但计算方法难以应用于几乎没有数据的多组分、无定形、统计共聚物材料。在此，利用一个包含101个示例的数据集，我们引入了一种闭环实验、机器学习（ML）和虚拟筛选策略，以实现对含有可裂解双官能甲硅烷基醚（BSE）共聚单体和/或交联剂且组成和含量各异的聚双环戊二烯（pDCPD）热固性材料的玻璃化转变温度（）的预测。分子特征和配方变量用作模型输入，并通过模型集成对不确定性进行量化，这与重度正则化一起有助于避免过拟合，并最终对具有不同组成的BSE的热固性材料实现<15°C的预测。这项工作为基于分子构建块预测热固性材料的性能提供了一条途径，这可能会加速发现具有改进功能和可控可解构性的有前景的塑料、橡胶和复合材料。
