Data-Driven Analysis of Ni-Catalyzed Semihydrogenations of Alkynes

The semihydrogenation of alkynes to alkenes has historically been an essential technique in organic chemistry. In this context, researchers often employ transition metal complexes to achieve this conversion. Given the pronounced polarization of results, often yielding either very high or very low values, it remains challenging to discern the factors influencing reactivity and selectivity in many cases. In this work, we combine different sub-disciplines of digital chemistry with experimental outcomes to rationalize the results of a model Ni-catalyzed semihydrogenation that leads to E-alkenes. First, we analyze the main factors behind successful reactions using a machine learning classification model. The descriptors are computed directly from the SMILES strings of the reacting alkynes using an automated protocol that relies on structural features, molecular mechanics, and semi-empirical techniques. This workflow requires minimal human intervention and provides a fast and effective approach. Next, we couple the same descriptors with activation barriers calculated with density functional theory, generating a regression model that explains reactivity based on the properties of the alkyne substrates. Overall, this study demonstrates the potential of using a combination of digital chemistry techniques to uncover reaction trends in Ni-catalyzed semihydrogenations of alkynes, an area where human intuition proves limited in application.