Accelerated dinuclear palladium catalyst identification through unsupervised machine learning.
Although machine learning bears enormous potential to accelerate developments in homogeneous catalysis, the frequent need for extensive experimental data can be a bottleneck for implementation. Here, we report an unsupervised machine learning workflow that uses only five experimental data points. It makes use of generalized parameter databases that are complemented with problem-specific in silico data acquisition and clustering. We showcase the power of this strategy for the challenging problem of speciation of palladium (Pd) catalysts, for which a mechanistic rationale is currently lacking. From a total space of 348 ligands, the algorithm predicted, and we experimentally verified, a number…
A Next‐Generation Air‐Stable Palladium(I) Dimer Enables Olefin Migration and Selective C−C Coupling in Air
Abstract We report a new air‐stable PdI dimer, [Pd(μ‐I)(PCy2 tBu)]2, which triggers E‐selective olefin migration to enamides and styrene derivatives in the presence of multiple functional groups and with complete tolerance of air. The same dimer also triggers extremely rapid C−C coupling (alkylation and arylation) at room temperature in a modular and triply selective fashion of aromatic C−Br, C−OTf/OFs, and C−Cl bonds in poly(pseudo)halogenated arenes, displaying superior activity over previous PdI dimer generations for substrates that bear substituents ortho to C−OTf.
CCDC 2064863: Experimental Crystal Structure Determination
Related Article: Julian H��ffel, Theresa Sperger, Ignacio Funes-Ardoiz, Jas Ward, Kari Rissanen, Franziska Schoenebeck|2021|Science|374|1134|doi:10.1126/science.abj0999
CCDC 2008108: Experimental Crystal Structure Determination
Related Article: Gourab Kundu, Theresa Sperger, Kari Rissanen, Franziska Schoenebeck|2020|Angew.Chem.,Int.Ed.|59|21930|doi:10.1002/anie.202009115