Precise Regulation on the Bond Dissociation Energy of Exocyclic C–N Bonds in Various N‑Heterocycle Electron Donors via Machine Learning

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.