Modern electronic structure methods have made robust predictions of stereo- and regioselectivies of many transition metal catalyzed reactions possible through the computation of key transition state (TS) structures. However, the requirement to sample large numbers of conformations and configurations of these TS structures often renders such predictions impractical. We will describe the open-source computational toolkit AARON (An Automated Reaction Optimizer for New catalysts), which provides automated predictions of selectivities for transition metal catalyzed reactions through automated quantum mechanical optimizations of TS structures based on previously computed structures. In this way, AARON can provide rapid predictions of selectivities across libraries of ligands, substrates, and methods, opening the door for the identification of new catalysts through virtual screening and the benchmarking of DFT methods for the prediction of selectivities. Representative applications of AARON to asymmetric hydrogenations and CH activation reactions will be presented.

Automated selectivity predictions for transition metal catalyzed reactions

2019 Fall National Meeting

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