Artificial intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze. AI, based largely on machine-learning algorithms that can mine huge data sets for patterns and correlations, seem best regarded as an assistant to, rather than a replacement for, the human researcher. It can do an awful lot, especially when coupled to robotic systems: not just analyse data but plan and execute experiments, make iterative improvements and even formulate and test specific hypotheses. Little of this is yet routine in the laboratory, but it is becoming ever more so. In some ways, chemistry is ripe for AI colonisation.

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.