Unsupervised machine-learning tool could accelerate catalyst discovery
The approach was able to identify phosphine ligands that may form dinuclear palladium(I) complexes using only five experimental data points
Combining machine learning with computationally derived descriptors has allowed scientists to find new examples of a special class of catalyst using only a few experimental data points. The team led by Franziska Schoenebeck from RWTH Aachen University in Germany developed a workflow that identified 21 phosphine ligands that may form dinuclear palladium(I) complexes with a certain geometry and air stability over the more common palladium(0) and palladium(II) species.1