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Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)–catalyzed carbon-nitrogen (C–N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C–N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows.

 

Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)–catalyzed carbon-nitrogen (C–N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C–N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows. 

Abstract Enantioselective diamination of alkenes represents one of the most straightforward methods to access enantioenriched, vicinal diamines, which are not only frequently encountered in biologically active compounds, but also have broad applications in asymmetric synthesis. Although the analogous dihydroxylation of olefins is well-established, the development of enantioselective olefin diamination lags far behind. Nevertheless, several successful methods have been developed that operate by different reaction mechanisms, including a cycloaddition pathway, a two-electron redox pathway, and a radical pathway. This short review summarizes recent advances and identifies limitations, with the aim of inspiring further developments in this area. 1 Introduction 2 Cycloaddition Pathway 3 Two-Electron Redox Pathway 3.1 Pd(0)/Pd(II) Diamination 3.2 Pd(II)/Pd(IV) Diamination 3.3 I(I)/I(III) Diamination 3.4 Se(II)/Se(IV) Diamination 4 One-Electron Radical Pathway 4.1 Cu-Catalyzed Diamination 4.2 Fe-Catalyzed Diamination 5 Summary and Outlook 

The application of machine learning (ML) to problems in homogeneous catalysis has emerged as a promising avenue for catalyst optimization. An important aspect of such optimization campaigns is determining which reactions to run at the outset of experimentation and which future predictions are the most reliable. Herein, we explore methods for these two tasks in the context of our previously developed chemoinformatics workflow. First, different methods for training set selection for library-based optimization problems are compared, including algorithmic selection and selection informed by unsupervised learning methods. Next, an array of different metrics for assessment of prediction confidence are examined in multiple catalyst manifolds. These approaches will inform future computer-guided studies to accelerate catalyst selection and reaction optimization. Finally, this work demonstrates the generality of the average steric occupancy (ASO) and average electronic indicator field (AEIF) descriptors in their application to transition metal catalysts for the first time.