Predicting the Reactivity of Acyclic Silylenes and Germylenes in Hydrogen Activation Using Machine Learning

Heavier carbenes can mimic transition metals in activating small molecules and strong bonds. Incorporating this ability into catalytic cycles demands careful, experimentally challenging tuning of their molecular properties via substituent variation. This study employs computational chemistry and data science to investigate dihydrogen activation by heavier carbenes to aid future candidate selection. Analyzing around 600 acyclic silylenes and germylenes derived from 40 different substituents, we found that a simple linear model predicts the activation barrier and reaction energy of H₂ activation based solely on increments specific for the elements in the α-position to the tetrel centre. We also established a multiple linear regression model, providing insights into quantitative structure-reactivity relationships. While initially developed for H₂ activation with silylenes, we demonstrate that this approach also is applicable to H₂ activation with germylenes and H₂O activation with silylenes, demonstrating its versatility in small molecule activation. The predicted activation energies were experimentally verified through kinetic studies, which confirmed the validity of our models and allowed us to identify candidates with promising substitution patterns. In doing so, we aim to inform the design of carbene-like species capable of small-molecule activation, thereby accelerating the development of main-group catalysts.