Machine Learning-Guided Development of Trialkylphosphine Ni(I) Dimers and Applications in Site-Selective Catalysis.

Owing to the unknown correlation of a metal's ligand and its resulting preferred speciation in terms of oxidation state, geometry, and nuclearity, a rational design of multinuclear catalysts remains challenging. With the goal to accelerate the identification of suitable ligands that form trialkylphosphine-derived dihalogen-bridged Ni(I) dimers, we herein employed an assumption-based machine learning approach. The workflow offers guidance in ligand space for a desired speciation without (or only minimal) prior experimental data points. We experimentally verified the predictions and synthesized numerous novel Ni(I) dimers as well as explored their potential in catalysis. We demonstrate C-I selective arylations of polyhalogenated arenes bearing competing C-Br and C-Cl sites in under 5 min at room temperature using 0.2 mol % of the newly developed dimer, [Ni(I)(µ-Br)PAd2(n-Bu)]2, which is so far unmet with alternative dinuclear or mononuclear Ni or Pd catalysts.

Rapid and Modular Access to Vinyl Cyclopropanes Enabled by Air-stable Palladium(I) Dimer Catalysis.

While vinyl cyclopropanes are valuable functional groups in drugs or natural products as well as established precursors to trigger a rich variety of synthetic transformations, their reactive nature can make their installation via direct catalytic approaches challenging. We herein present a modular access to (di)vinyl cyclopropanes under very mild conditions and full conservation of stereochemistry, allowing access to the cis or trans cyclopropane- as well as E or Z vinyl-stereochemical relationships. Our protocol relies on air-stable dinuclear PdI catalysis, which enables rapid (<30 min) and selective access to a diverse range of vinyl cyclopropane motifs at room temperature, even on gram scale.