Makki Hesam, Burke Colm, Nielsen Christian B, Troisi Alessandro
Department of Chemistry, University of Liverpool, Liverpool L69 3BX, UK.
Department of Chemistry, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
Mater Horiz. 2025 May 20. doi: 10.1039/d5mh00485c.
The molecular design of semiconducting polymers (SCPs) has been largely guided by varying monomer combinations and sequences by leveraging a robust understanding of charge transport mechanisms. However, the connection between controllable structural features and resulting electronic disorder remains elusive, leaving design rules for next-generation SCPs undefined. Using high-throughput computational methods, we analyse 100+ state-of-the-art p- and n-type polymer models. This exhaustive dataset allows for deriving statistically significant design rules. Our analysis disentangles the impact of key structural features, examining existing hypotheses, and identifying new structure-property relationships. For instance, we show that polymer rigidity has minimal impact on charge transport, while the planarity persistence length, introduced here, is a superior structural characteristic. Additionally, the predictive power of machine learning models trained on our dataset highlights the potential of data-driven approaches to SCP design, laying the groundwork for accelerated discovery of materials with tailored electronic properties.
半导体聚合物（SCP）的分子设计在很大程度上是通过利用对电荷传输机制的深入理解来改变单体组合和序列来指导的。然而，可控结构特征与由此产生的电子无序之间的联系仍然难以捉摸，使得下一代SCP的设计规则尚不明确。我们使用高通量计算方法，分析了100多个先进的p型和n型聚合物模型。这个详尽的数据集有助于得出具有统计学意义的设计规则。我们的分析理清了关键结构特征的影响，审视了现有假设，并确定了新的结构-性能关系。例如，我们表明聚合物刚性对电荷传输的影响最小，而本文引入的平面持久长度是一种更优越的结构特征。此外，基于我们的数据集训练的机器学习模型的预测能力凸显了数据驱动方法在SCP设计中的潜力，为加速发现具有定制电子特性的材料奠定了基础。
