Accelerated dinuclear palladium catalyst identification through unsupervised machine learning.

Abstract

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 of phosphine ligands (including previously never synthesized ones) that give dinuclear Pd^(I) complexes over the more common Pd⁽⁰⁾ and Pd^(II) species.