Search for: All records

Abstract This study reports the development and application of a conformer‐dependent quantitative quadrant descriptor for generating models that relate catalyst performance to catalyst structure in enantioselective transformations. The generality of these descriptors is demonstrated by using them for three different reactions: (1) copper‐catalyzed, enantioselective cyclopropanation of alkenes, (2) rhodium‐catalyzed enantioselective hydrogenation ofα‐substitutedN‐acyl‐enamides, and (3) enantioselective addition of thiols toN‐acyl imines. This work will provide researchers an interpretable steric descriptor that merges the heuristic value of quadrant models with a quantitative tool that can be used to create statistically meaningful correlations between the steric occupancy of catalyst quadrants and stereoselectivity. The low dimensionality of this descriptor, its ability to capture conformational effects and stereostructure, and its direct relationship to intuitive structural properties make it particularly well‐suited for creating Quantitative Structure‐Selectivity Relationships (QSSR) with smaller datasets of asymmetric reactions. 

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. 

Catalyst optimization for enantioselective transformations has traditionally relied on empirical evaluation of catalyst properties. Although this approach has been successful in the past is it intrinsically limtied and inefficient.  To address this problem, our laboratory has developed a fully informatics guided workflow to leverage the power of artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and optimization any class of catalyst for any transformation. This approach is mechanistically agnostic, but also serves as a discovery platform to identify high performing catalysts that can be subsequently investigated by with physical organic methods to identify the origins of selectivity. 

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.