Anionic reagents with silicon-containing double bonds, M(R)Si═ERn (E = main group elements), have garnered significant interest owing to their unique metal-mediated reactivity and their potential in transferring the Si═E unit. Within this domain, the intriguing field of Si-metalated phosphasilenes remains uncharted.

Adaptive aromaticity has attracted substantial attention due to its unconventional two-state aromaticity in both the lowest singlet state (S0) and the lowest triplet state (T1) and its application to the singlet fission. Here we carry out a comprehensive investigation on a series of pyrrole derivatives by density functional theory (DFT) calculations and machine learning.