No results can be a result too, chemists from Delft prove. They publish one of the largest datasets of a model of rhodium-catalysed hydrogenation that showed surprisingly little, as shown in Chemical Science.

For half a century, rhodium catalysts have been used for the enantioselective hydrogenation of alkenes. Countless articles have been published on this type of catalyst and the underlying reaction mechanism, so one might expect that there is a well-developed understanding of these catalytic reactions. But there is no streamlined way to quickly select the right ligands for your homogeneous catalyst when changing substrates. Adarsh Kalikadien, Evgeny Pidko and colleagues at TU Delft and Janssen Pharmaceutica wanted to see if they could use machine learning to develop a predictive model for this, but the project turned out differently than expected.