Machine Learning

Antiaromaticity-Promoted Radical Stability in Boryl Heterocyclics and Its Application to Dinitrogen Activation: A Combined DFT and Machine Learning Study

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.

Charge-driven stability and aromaticity of C₂N₂B₂H₄ isomers: Insights from a combined DFT and machine learning study

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.

Probing the Origin of Higher Efficiency of Terphenyl Phosphine over the Biaryl Framework in Pd-catalyzed C-N Coupling: A Combined DFT and Machine Learning Study

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.