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<p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H-
pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a
training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The
selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized
and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the
catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl-
4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development.
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Data-driven Scenario Selection for Multistage Robust Model Predictive Control
