An Artificial Intelligence that Discovers Unpredictable Chemical Reactions Dario Caramelli, Jarosław M. Granda, Dario Cambié, S. Hessam M. Mehr, Alon Henson and Leroy Cronin* School of Chemistry, the University of Glasgow, University Avenue, Glasgow G12 8QQ, UK. *Correspondence to:
[email protected]. The robot-driven detection of novel organic chemical reactions1-2 is difficult as there is no approach that guarantees discovery on demand3. Traditional approaches to find new chemical reactions often rely on human error, whilst theoretical4,5 and artificial intelligence6-8 approaches promise new targets, but these must be identified computationally9,10. However, it is very hard to turn these ideas into reality in a chemistry laboratory, and the targets are not often novel. Herein, we present an artificial intelligence, built to autonomously explore chemical reactions in the laboratory using deep learning11. The reactions are performed automatically, analysed online, and the data is processed using a convolutional neural network (CNN) trained on a small reaction dataset to assess the reactivity of reaction mixtures12. The network can be used to predict the reactivity of an unknown dataset, meaning that the system is able to abstract the reactivity assignment regardless the identity of the starting materials. The system was set up with 15 inputs that were combined in 1018 reactions, the analysis of which lead to the discovery of a ‘multi-step, single-substrate’ cascade reaction and a new mode of reactivity for methylene isocyanides. p-Toluenesulfonylmethyl isocyanide (TosMIC) in presence of an activator reacts consuming six equivalents of itself to yield a trimeric product in high (unoptimized) yield (47%) with formation of five new C-C bonds involving sp-sp2 and sp- sp3 carbon centres.