Smith Lois, Karimi-Varzaneh Hossein Ali, Finger Sebastian, Giunta Giuliana, Troisi Alessandro, Carbone Paola
Department of Chemical Engineering, School of Engineering, The University of Manchester, Oxford Road, M13 9PL Manchester, U.K.
Continental Reifen Deutschland GmbH, Jädekamp 30, D-30419 Hanover, Germany.
Macromolecules. 2024 May 14;57(10):4637-4647. doi: 10.1021/acs.macromol.3c01764. eCollection 2024 May 28.
Polymer composite materials require softening to reduce their glass transition temperature and improve processability. To this end, plasticizers (PLs), which are small organic molecules, are added to the polymer matrix. The miscibility of these PLs has a large impact on their effectiveness and, therefore, their interactions with the polymer matrix must be carefully considered. Many PL characteristics, including their size, topology, and flexibility, can impact their miscibility and, because of the exponentially large number of PLs, the current trial-and-error approach is very ineffective. In this work, we show that using coarse-grained molecular simulations of a small dataset of 48 PLs, it is possible to identify topological and thermodynamic descriptors that are proxy for their miscibility. Using molecular dynamics simulation setups that are relatively computationally inexpensive, we establish correlations between the PLs' topology, internal flexibility, thermodynamics of aggregation, and degree of miscibility, and use these descriptors to classify the molecules as miscible or immiscible. With all available data, we also construct a decision tree model, which achieves a F1 score of 0.86 ± 0.01 with repeated, stratified 5-fold cross-validation, indicating that this machine learning method can be a promising route to fully automate the screening. By evaluating the individual performance of the descriptors, we show this procedure enables a 10-fold reduction of the test space and provides the basis for the development of workflows that can efficiently screen PLs with a variety of topological features. The approach is used here to screen for apolar PLs in polyisoprene melts, but similar proxies would be valid for other polyolefins, while, in cases where polar interactions drive the miscibility, other descriptors are likely to be needed.
聚合物复合材料需要软化以降低其玻璃化转变温度并提高加工性能。为此，将作为小分子有机化合物的增塑剂添加到聚合物基体中。这些增塑剂的混溶性对其有效性有很大影响，因此，必须仔细考虑它们与聚合物基体的相互作用。许多增塑剂特性，包括其大小、拓扑结构和柔韧性，都会影响其混溶性，而且由于增塑剂的数量呈指数级增长，目前的试错方法效率非常低。在这项工作中，我们表明，通过对48种增塑剂的小数据集进行粗粒度分子模拟，可以识别出作为其混溶性代理的拓扑和热力学描述符。使用计算成本相对较低的分子动力学模拟设置，我们建立了增塑剂的拓扑结构、内部柔韧性、聚集热力学和混溶度之间的相关性，并使用这些描述符将分子分类为可混溶或不可混溶。利用所有可用数据，我们还构建了一个决策树模型，在重复的分层5折交叉验证中，该模型的F1分数为0.86±0.01，这表明这种机器学习方法可能是实现完全自动化筛选的一条有前途的途径。通过评估描述符的个体性能，我们表明该程序能够将测试空间减少10倍，并为开发能够有效筛选具有各种拓扑特征的增塑剂的工作流程提供基础。这里使用该方法在聚异戊二烯熔体中筛选非极性增塑剂，但类似的代理对其他聚烯烃也有效，而在极性相互作用驱动混溶性的情况下，可能需要其他描述符。
