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Issue 20, 2017
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Designing bifunctional alkene isomerization catalysts using predictive modelling

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Controlling the isomerization of alkenes is important for the manufacturing of fuel additives, fine-chemicals and pharmaceuticals. But even if isomerization seems to be a simple unimolecular process, the factors that govern catalyst performance are far from clear. Here we present a set of models that describe selectivity and activity, enabling the rational design and synthesis of alkene isomerization catalysts. The models are based on simple molecular descriptors, with a low computational cost, and are tested and validated on a set of eleven known Ru-imidazol-phosphine complexes and two new ones. Despite their simplicity, these models show good predictive power, with R2 values of 0.60–0.85. Using a combination of principal components analysis (PCA) and partial least squares (PLS) regression, we construct a “catalyst map”, that captures trends in reactivity and selectivity as a function of electrostatic charge on the N* atom, EHOMO, polar surface area and the optimal mass substituents on P/distance Ru–P ratio. In addition to indicating “good regions” in the catalyst space, these models also give insight into mechanistic steps. For example, we find that the electrostatic charge on N*, EHOMO and polar surface area are crucial in the rate-limiting step, whereas the optimal mass of substituents on P/distance Ru–P is correlated with the product selectivity.

Graphical abstract: Designing bifunctional alkene isomerization catalysts using predictive modelling

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Publication details

The article was received on 02 Jun 2017, accepted on 19 Sep 2017 and first published on 20 Sep 2017

Article type: Paper
DOI: 10.1039/C7CY01106G
Citation: Catal. Sci. Technol., 2017,7, 4842-4851
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    Designing bifunctional alkene isomerization catalysts using predictive modelling

    I. R. Landman, E. R. Paulson, A. L. Rheingold, D. B. Grotjahn and G. Rothenberg, Catal. Sci. Technol., 2017, 7, 4842
    DOI: 10.1039/C7CY01106G

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