Proximity Learning-Enabled Ligand Prediction for Ni-Catalyzed Atroposelective Suzuki-Miyaura Cross-Coupling: Leveraging Pd Catalysis Knowledge for Ni Discovery

The rational design of novel molecular catalysts often confronts challenges due to complex structure-performance relationships. Emerging data-driven approaches provide revolutionary solutions, yet the application of machine learning to new catalyst development inevitably faces a low-data regime, with limited effective structure-performance modeling available. In this study, we present a proximity learning strategy to facilitate knowledge transfer from well-documented Pd catalysis to novel, underexplored Ni systems. By synergistically modeling extensive palladium catalysis data with limited nickel/SadPhos data, our approach accurately predicted novel SadPhos ligands, enabling the first atroposelective nickel-catalyzed Suzuki-Miyaura cross-coupling reaction. The synthetic utility of the machine learning-predicted ligand was further demonstrated in the broad synthetic scope, gram-scale synthesis, and precise control of dual axial chiralities in ternaphthalene through the sequential coupling under Ni and Pd catalysis. Additionally, density functional theory calculations were employed to reveal the reaction mechanism and stereochemical model of this new catalytic system, validating the proposed mechanistic connection between Ni and Pd. This work demonstrates how machine learning models can effectively leverage mechanistic connectivity, applying extensive structure-performance relationship data from the literature to predict new catalysts, providing a novel strategy for the rational design of molecular catalysts from a few-shot learning perspective.