Abstract
In this work, we present our efforts to develop a Grignard reagent free, low valent cobalt-catalysed C-H arylation, using high-throughput experimentation (HTE). Although we did not succeed to obtain a protocol with synthetically relevant yields, we believe that this data will be valuable for researchers working in the same domain. Additionally, these data will be useful considering the current trend to develop machine learning methods for predicting or optimizing new transformations. Such efforts frequently face the challenge, that data for training sets mainly consist of positive results. However, a good training set needs to include a significant number of negative results as well. Sharing such data can reduce bias in machine learning models and help expand our understanding of chemical space through more complete and realistic datasets.