Charge-Promoted Adaptive Aromaticity in Metallabenzenes: A Combined DFT and Machine Learning Study

In accordance with the constraints by Hückel’s and Baird’s rules, species generally exhibit aromaticity in one state (the lowest singlet state S 0 or the lowest triplet state T 1 ). Consequently, species with adaptive aromaticity (being aromatic in both the S 0 and T 1 states) are particularly rare. In this study, density functional theory (DFT) was employed to investigate adaptive aromaticity in 14e –, 16e – and 18e – metallabenzenes. Based on bond length, fuzzy bond order (FBO), multicenter index (MCI), delocalized bond electron density (EDDB) analyses, magnetically induced current-density (MICD) analysis, and isomerization stabilization energy (ISE), 14e – ruthenabenzenes are found to possess adaptive aromaticity. A reduction in the delocalization of the spins in the six-membered ring of metallabenzenes will facilitate the maintenance of their T 1 aromaticity. The ligand’s effect on adaptive aromaticity in 14e – ruthenabenzenes was conducted by principal component analysis (PCA), one of the most commonly used unsupervised machine learning algorithms, which suggests that electron-deficient ligands are detrimental to adaptive aromaticity. A supervised machine learning method, multiple linear regression (MLR), was employed to develop a quantitative equation for predicting aromaticity in the T 1 state. This study demonstrates the first example of a metallabenzene with adaptive aromaticity, which is a valuable contribution to the field of aromatic chemistry.