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 ad-hoc 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.

中文翻译：

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评估聚合物/增塑剂混溶性的高通量筛选方法框架：聚烯烃中碳氢化合物的案例


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，将增塑剂（PL）这种有机小分子添加到聚合物基质中。这些 PL 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质的相互作用。许多 PL 特性，包括它们的大小、拓扑和灵活性，都会影响它们的混溶性，并且由于 PL 的数量呈指数级增长，当前的试错方法非常无效。在这项工作中，我们表明，使用 48 个 PL 的小数据集的粗粒度分子模拟，可以识别代表其混溶性的拓扑和热力学描述符。使用计算成本相对较低的临时分子动力学模拟装置，我们建立了 PL 的拓扑结构、内部灵活性、聚集热力学和混溶程度之间的相关性，并使用这些描述符将分子分类为混溶或不混溶。利用所有可用数据，我们还构建了一个决策树模型，通过重复的分层 5 倍交叉验证，该模型的 F1 分数达到 0.86 ± 0.01，这表明这种机器学习方法可以成为完全自动化筛选的一条有前途的途径。通过评估描述符的单独性能，我们表明该过程可以将测试空间减少 10 倍，并为开发可以有效筛选具有各种拓扑特征的 PL 的工作流程提供基础。 该方法用于筛选聚异戊二烯熔体中的非极性 PL，但类似的替代方法对于其他聚烯烃也有效，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。