"Using High Throughput Experimentation to Gain New Insights"

Nature uses oxidative couplings to construct carbon-carbon, carbon-oxygen, and carbon-nitrogen bonds with a high degree of efficiency. The use of parallel microscale screening to discover selective and efficient catalysts for such processes using oxygen as the terminal oxidant will be discussed. With the desire to generate further large experimental data sets to drive machine learning, methods are needed to rapidly determine outcomes for a large numbers of diverse reactions. However, numerous technological and reproducibility hurdles limit the types of data sets that can be generated and their fidelity. Approaches to overcome these limitations will be discussed.