An Artificial Intelligence that Discovers Unpredictable Chemical Reactions
semanticscholar(2020)
摘要
We present an artificial
intelligence, built to autonomously explore chemical reactions in the
laboratory using deep learning. 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
mixtures. 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. A cheminformatics analysis reveals that this transformation is both
highly unpredictable and able to generate an increase in complexity like a
one-pot multicomponent reaction.
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