Yield-predicting AI needs chemists to stop ignoring failed experiments

Machine-learning algorithms that can predict reaction yields have remained elusive because chemists tend to bury low-yielding reactions in their lab notebooks instead of publishing them, researchers say. 'We have this image that failed experiments are bad experiments,' says Felix Strieth-Kalthoff. 'But they contain knowledge, they contain valuable information both for humans and for an AI.' Strieth-Kalthoff from the University of Toronto, Canada, and a team around Frank Glorius from Germany's University of Münster are asking chemists to start including not only their best but also their worst results in their papers. This, as well as unbiased reagent selection and reporting experimental procedures in a standardised format, will allow researchers to finally create yield-prediction algorithms. Retrosynthesis is already using machine-learning models to create shorter, cheaper or non-proprietary synthetic routes. But there have been few attempts at creating programs that predict yields.