Heterocycles with saturated N atoms (HetSNs) are widely used electron donors in organic light-emitting diode (OLED) materials. Their relatively low bond dissociation energy (BDE) of exocyclic C–N bonds has been closely related to material intrinsic stability and even device lifetime. Thus, it is imperative to realize fast prediction and precise regulation of those C–N BDEs, which demands a deep understanding of the relationship between the molecular structure and BDE. Herein, via machine learning (ML), we rapidly and accurately predicted C–N BDEs in various HetSNs and found that five-membered HetSNs (5-HetSNs) have much higher BDEs than almost all 6-HetSNs, except emerging boron–N blocks. Thorough analysis disclosed that high aromaticity is the foremost factor accounting for the high BDE of 5-HetSNs, and introducing intramolecular hydrogen-bond or electron-withdrawing moieties could also increase BDE. Importantly, the ML models performed well in various realistic OLED materials, showing great potential in characterizing material intrinsic stability for high-throughput virtual-screening and material design efforts.

中文翻译：

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通过机器学习精确调控各种N杂环电子给体中环外C-N键的键解离能

具有饱和氮原子的杂环（HetSN）是有机发光二极管（OLED）材料中广泛使用的电子供体。它们的环外C-N键相对较低的键解离能（BDE）与材料的内在稳定性甚至器件寿命密切相关。因此，实现对C-N BDE的快速预测和精确调控势在必行，这需要深入了解分子结构与BDE之间的关系。在这里，通过机器学习（ML），我们快速准确地预测了各种HetSN中的C-N BDE，发现五元HetSN（5-HetSN）的BDE比几乎所有6-HetSN高得多，除了新兴的硼-N块之外。深入分析表明，高芳香性是导致5-HetSNs高BDE的首要因素，引入分子内氢键或吸电子基团也可能增加BDE。重要的是，ML 模型在各种现实 OLED 材料中表现良好，在表征材料内在稳定性以进行高通量虚拟筛选和材料设计工作方面显示出巨大潜力。