Hypervalent iodine reagents are increasingly gaining attention in the fields of organic synthesis and catalysis due to their environmental benefits, accessibility, and cost-efficiency . Over the last three decades, a series of cyclic hypervalent iodine(III) reagents has been developed (Figure 1), including the well-known Zhdankin reagents and Togni reagents . These reagents are popularly used as electrophilic group transfer reagents in a variety of reactions, such as C–H functionalization , unsaturated alkane addition , and cyclization .
Figure 1: Examples of cyclic hypervalent halogen reagents.
Despite the rapid development of hypervalent iodine(III) reagents, the exploration of isoelectronic hypervalent bromine(III) and chlorine(III) reagents has been comparatively limited despite their demonstrated potential for unique applications . For example, hypervalent bromine(III) reagents enable C–H amination and alkene aziridination reactions without the need for additional Lewis acid activation . However, challenges in the synthesis and stabilization of cyclic hypervalent bromine and chlorine reagents have impeded their development relative to their iodine(III) analogs . Cyclic hypervalent bromine(III) reagents were pioneered by Miyamoto and have since been developed to a certain extent . Biphenyl hypervalent bromine(III) reagents have been synthesized by Yoshida and Wencel-Delord (Figure 1). Cyclic hypervalent chlorine(III) reagents with similar skeletal structures have not been reported yet, and only biphenyl hypervalent chlorine(III) reagents and cyclic diaryliodonium salts have been synthesized.
Previous investigations have highlighted the critical role of bond dissociation energy (BDE) in understanding the group transfer capabilities and chemical stability of hypervalent iodine(III) reagents. In this context, detailed knowledge of the BDE of hypervalent bromine(III) and chlorine(III) reagents is especially crucial for designing novel reagents. Yet, the BDE values of hypervalent bromine(III) and chlorine(III) reagents remain largely elusive, hampering the design and synthesis of novel reagents.
In recent years, machine learning has emerged as a promising and cost-effective alternative to traditional DFT calculations for predicting key properties of organic molecules such as BDE, nucleophilicity, and electrophilicity . Recently, applications of the Elastic Net model with Avalon fingerprints and the deployment of artificial neural network (ANN) models with the Mordred cheminformatics package have demonstrated considerable success in predicting the BDEs of hypervalent iodine(III) reagents. However, previous studies have been limited to the prediction of hypervalent iodine(III) reagents. Driven by their proven effectiveness and our ongoing interest in hypervalent halogen chemistry , we are motivated to develop a machine learning model for a broader array of cyclic hypervalent halogen reagents, thereby integrating different halogen centers and making it easier to predict the group transfer capacity and chemical stability of different cyclic hypervalent halogen reagents.
Results and DiscussionWe selected five different skeletons and twenty common transfer groups for combination (Figure 2) and calculated their BDEs. Referring to the previous computational studies of hypervalent iodine and the computational database of organic species by Paton and co-workers , geometry optimizations and single point energy calculations for homolytic BDEs are both performed using M06-2X/def2-TZVPP in the gas phase at 298.15 K by Gaussian 16 . Frequency calculations confirmed that optimized structures are minima (no imaginary frequency). The accuracy of computational BDEs of halides using M06-2X/def2-TZVPP is also evaluated and compared to experimental BDEs, demonstrating the reliability of the method (see Supporting Information File 1).
Figure 2: Common cyclic hypervalent halogen skeletons and transfer groups.
The computational homolytic BDEs are presented in Table 1. From the perspective of halogen centers, hypervalent iodine(III) reagents exhibit the highest homolytic BDEs, followed by hypervalent bromine(III) reagents, while hypervalent chlorine(III) reagents have the lowest. Generally, the homolytic BDEs of cyclic hypervalent iodine(III) reagents are above 30.0 kcal/mol, consistent with their good chemical stability. The homolytic BDEs of some cyclic hypervalent bromine(III) and most cyclic hypervalent chlorine(III) reagents are below 20 kcal/mol, implying these reagents should be too reactive to be isolated. From the perspective of transfer groups, the homolytic BDEs of groups with strong trans effects such as -F, -CCH, -CN, -OCF3, -OTf, -OTs are elevated, while those of -N3, -NH2, -SCF3, etc. are smaller. These results are consistent with our previous studies on the group transfer ability of hypervalent iodine(III) reagents . According to the calculation results, skeleton 5 may be a better candidate for synthesizing cyclic hypervalent bromine(III) and chlorine(III) reagents. The groups with strong trans effects, such as -F, -CCH, -CN, -OTf, can help stabilize cyclic hypervalent bromine(III) and chlorine(III) reagents.
Table 1: Computational homolytic BDEs (kcal/mol) of cyclic hypervalent halogen reagents.
In addition, we also calculated the heterolytic BDEs of cyclic hypervalent halogen reagents to comprehensively examine the strength of chemical bonds (Table 2). Geometry optimizations and single point energy calculations for heterolytic BDEs are performed using M06-2X/def2-TZVPP in the SMD (acetonitrile) Implicit solvent model at 298.15 K. Due to the instability of some transfer group cations, such as +OCH3, +OCF3, +OCOCF3, +OCOPh, +OTf and +SCF3, it is difficult for us to investigate their heterolytic BDEs. From Table 2, it can be seen that, except for CF3 and CHCH2, all other transfer groups exhibit high heterolytic BDEs with hypervalent halogen centers.
Table 2: Computational heterolytic BDEs (kcal/mol) of cyclic hypervalent halogen reagents.
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