Transfer Learning-EnabledLigand Prediction for Ni-CatalyzedAtroposelective Suzuki–Miyaura Cross-Coupling Based on MechanisticSimilarity: Leveraging Pd 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 modelings available. In this study, we present a transfer learning strategy to facilitate knowledge transfer from well-documented Pd catalysis to a novel, underexplored Ni system. By synergistically modeling extensive Pd catalysis data with limited Ni/Sadphos data, our approach accurately predicted novel Sadphos ligands, enabling the first atroposelective Ni-catalyzed Suzuki–Miyaura cross-coupling reaction. The synthetic utility of the machine learning-predicted ligand was further demonstrated in its 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 Ni catalyst, 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.