Mapping binary copolymer property space with neural networks
Mapping binary copolymer property space with neural networks
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DOI：
10.1039/c8sc05710a
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发表时间：
2019-05-21
期刊：
影响因子：
8.4
通讯作者：
Zwijnenburg, Martijn A.
中科院分区：
文献类型：
--
作者：
Wilbraham, Liam;Sprick, Reiner Sebastian;Zwijnenburg, Martijn A.
通过共聚可以获得大量独特的聚合物组合物，这使其成为调节其光电性能的有吸引力的策略。然而，同样的属性也使得探索由此产生的财产空间和了解可以实现的财产范围变得具有挑战性。为了能够快速探索二元共聚物的这一空间，我们使用分层数据生成策略训练一个神经网络，以准确预测 350 000 个二元共聚物的光学和电子特性，这些共聚物原则上可以在一步功能化后通过 Yamamoto 或 Suzuki-Miyaura 和 Stille 耦合从其二卤单体合成。通过提取该属性空间的一般特征（否则这些特征在较小的数据集中会被掩盖），我们确定了简单的模型，可以有效地将这些共聚物的属性与其组成单体的均聚物联系起来，并挑战共聚物设计背后的常见想法。我们发现，二元共聚似乎不允许进入均聚物尚未采样的光电特性空间区域，尽管它在概念上允许更细粒度的特性控制。利用大量可用数据，我们测试了以下假设：“供体”和“受体”单体的共聚可以产生比相关均聚物具有更低光学间隙的共聚物。总的来说，尽管这个概念在文献中很普遍，但我们观察到这种现象相对罕见，并提出了大大提高其实验实现可能性的条件。最后，通过对共聚物性能空间的“拓扑”分析，我们展示了如何使用大量数据来识别性能空间特定区域中的主要单体，这些单体可能适合各种应用，例如有机光伏、发光二极管和热电。
The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350 000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers via Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers, and challenge common ideas behind copolymer design. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although it conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of ` donor' and ` acceptor' monomers can result in copolymers with a lower optical gap than their related homopolymers. Overall, despite the prevalence of this concept in the literature, we observe that this phenomenon is relatively rare, and propose conditions that greatly enhance the likelihood of its experimental realisation. Finally, through a ` topographical' analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics.