Predicting bond dissociation energies of cyclic hypervalent halogen reagents using DFT calculations and graph attention network model - Shao, Yingbo; Ren, Zhiyuan; Han, Zhihui; Chen, Li; Li, Yao; Xue, Xiao-Song (Beilstein Journal of Organic Chemistry, 2024)
Although hypervalent iodine(III) reagents have become staples in organic chemistry, the exploration of their isoelectronic counterparts, namely hypervalent bromine(III) and chlorine(III) reagents, has been relatively limited, partly due to challenges in synthesizing and stabilizing these compounds. In this study, we conduct a thorough examination of both homolytic and heterolytic bond dissociation energies (BDEs) critical for assessing the chemical stability and functional group transfer capability of cyclic hypervalent halogen compounds using density functional theory (DFT) analysis. A moderate linear correlation was observed between the homolytic BDEs across different halogen centers, while a strong linear correlation was noted among the heterolytic BDEs across these centers. Furthermore, we developed a predictive model for both homolytic and heterolytic BDEs of cyclic hypervalent halogen compounds using machine learning algorithms. The results of this study could aid in estimating the chemical stability and functional group transfer capabilities of hypervalent bromine(III) and chlorine(III) reagents, thereby facilitating their development.
Keywords: BDE; cyclic hypervalent halogen reagents; DFT calculation; graph attention network; machine learning