键离解能
分子内力
有机发光二极管
芳香性
氢键
离解(化学)
计算化学
化学
硼
极性效应
组合化学
材料科学
化学物理
光化学
立体化学
分子
物理化学
有机化学
图层(电子)
作者
Qingyu Meng,Rui Wang,Haoyun Shao,Yilei Wang,Xue-Liang Wen,Cheng-Yu Yao,Juan Qiao
标识
DOI:10.1021/acs.jpclett.4c00705
摘要
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.
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