In recent years, while main-group elements from the s- and p-block have emerged in the field of N2 activation, silylenes─despite their remarkable successes in the activation of diverse small molecules─remain unreported for N2 activation. Herein, we design “silylene-borole” frustrated Lewis pairs (FLPs) by combining silylene moieties with boron components and conduct comprehensive density functional theory (DFT) calculations to thoroughly investigate their potential for N2 activation.

The capture of carbon dioxide is extremely important due to the increasingly severe greenhouse effect, and the conversion of dinitrogen into high-value N–C compounds is of great significance. Here, we predict through density functional theory calculations that the coupling of dinitrogen with carbon dioxide by methyleneborane becomes favorable both thermodynamically and kinetically. Machine learning analysis suggests that increasing the HOMO–LUMO gap or the charge on the boron atom or decreasing the charge of the nitrogen atom will reduce the reaction energies.

Aromaticity is one of the fundamental concepts in chemistry and generally brings additional thermodynamic stability to a compound. On the other hand, boron radicals have attracted increasing interest from both theoretical and experimental chemists due to their various applications. Here, we carry out density functional theory (DFT) calculations to explore the relationship between the (anti)aromaticity and stability of boron-centered radicals.

Recent research has sparked significant interest in exploring the effects of BN unit doping on the electronic structure of isoelectronic and isostructural benzene analogs, driven by their promising applications in pharmaceuticals and material sciences. In this study, we provide the first comprehensive investigation of BN/CC isosterism in 2,5-dihydro-1,4,2,5-diazadiborinine and its isomers (1−13) through density functional theory (DFT) calculations and machine learning-based analysis.

The Pd-catalyzed Buchwald–Hartwig coupling reaction is important in the construction of the C-N bond due to various applications in organic synthesis. Quantum chemical calculations are widely used in understanding reaction mechanisms whereas the machine learning method is extremely popular in recognizing the relationships of data. Here, we combine density functional theory calculations with the support vector regression method to probe the origin of the higher efficiency of terphenyl phosphine ligand over the biaryl counterpart in the Buchwald–Hartwig C-N coupling reaction.