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Issue 35, 2020
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Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

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Abstract

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr–N distance, Cr–α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.

Graphical abstract: Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

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Supplementary files

Article information


Submitted
27 Jun 2020
Accepted
20 Aug 2020
First published
21 Aug 2020

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2020,11, 9665-9674
Article type
Edge Article

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

Steven M. Maley, D. Kwon, N. Rollins, J. C. Stanley, O. L. Sydora, S. M. Bischof and D. H. Ess, Chem. Sci., 2020, 11, 9665
DOI: 10.1039/D0SC03552A

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